FewShotPapers

Few-Shot Papers

This repository contains few-shot learning (FSL) papers mentioned in our FSL survey published in ACM Computing Surveys (JCR Q1, CORE A*).

For convenience, we also include public implementations of respective authors.

We will update this paper list to include new FSL papers periodically.

Citation

Please cite our paper if you find it helpful.

@article{wang2020generalizing,
  title={Generalizing from a few examples: A survey on few-shot learning},
  author={Wang, Yaqing and Yao, Quanming and Kwok, James T and Ni, Lionel M},
  journal={ACM Computing Surveys},
  volume={53},
  number={3},
  pages={1--34},
  year={2020},
  publisher={ACM New York, NY, USA}
}

Content

Survey

Generalizing from a few examples: A survey on few-shot learning, CSUR, 2020 Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni. paper arXiv

Data

Learning from one example through shared densities on transforms, in CVPR, 2000. E. G. Miller, N. E. Matsakis, and P. A. Viola. paper
Domain-adaptive discriminative one-shot learning of gestures, in ECCV, 2014. T. Pfister, J. Charles, and A. Zisserman. paper
One-shot learning of scene locations via feature trajectory transfer, in CVPR, 2016. R. Kwitt, S. Hegenbart, and M. Niethammer. paper
Low-shot visual recognition by shrinking and hallucinating features, in ICCV, 2017. B. Hariharan and R. Girshick. paper code
Improving one-shot learning through fusing side information, arXiv preprint, 2017. Y.H.Tsai and R.Salakhutdinov. paper
Fast parameter adaptation for few-shot image captioning and visual question answering, in ACM MM, 2018. X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu. paper
Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning, in CVPR, 2018. Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang. paper
Low-shot learning with large-scale diffusion, in CVPR, 2018. M. Douze, A. Szlam, B. Hariharan, and H. Jégou. paper
Diverse few-shot text classification with multiple metrics, in NAACL-HLT, 2018. M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou. paper code
Delta-encoder: An effective sample synthesis method for few-shot object recognition, in NeurIPS, 2018. E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein. paper
Low-shot learning via covariance-preserving adversarial augmentation networks, in NeurIPS, 2018. H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang. paper
Learning to self-train for semi-supervised few-shot classification, in NeurIPS, 2019. X. Li, Q. Sun, Y. Liu, S. Zheng, Q. Zhou, T.-S. Chua, and B. Schiele. paper
Few-shot learning with global class representations, in ICCV, 2019. A. Li, T. Luo, T. Xiang, W. Huang, and L. Wang. paper
AutoAugment: Learning augmentation policies from data, in CVPR, 2019. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le. paper
EDA: Easy data augmentation techniques for boosting performance on text classification tasks, in EMNLP and IJCNLP, 2019. J. Wei and K. Zou. paper
LaSO: Label-set operations networks for multi-label few-shot learning, in CVPR, 2019. A. Alfassy, L. Karlinsky, A. Aides, J. Shtok, S. Harary, R. Feris, R. Giryes, and A. M. Bronstein. paper code
Image deformation meta-networks for one-shot learning, in CVPR, 2019. Z. Chen, Y. Fu, Y.-X. Wang, L. Ma, W. Liu, and M. Hebert. paper code
Spot and learn: A maximum-entropy patch sampler for few-shot image classification, in CVPR, 2019. W.-H. Chu, Y.-J. Li, J.-C. Chang, and Y.-C. F. Wang. paper
Adversarial feature hallucination networks for few-shot learning, in CVPR, 2020. K. Li, Y. Zhang, K. Li, and Y. Fu. paper
Instance credibility inference for few-shot learning, in CVPR, 2020. Y. Wang, C. Xu, C. Liu, L. Zhang, and Y. Fu. paper
Diversity transfer network for few-shot learning, in AAAI, 2020. M. Chen, Y. Fang, X. Wang, H. Luo, Y. Geng, X. Zhang, C. Huang, W. Liu, and B. Wang. paper code
Neural snowball for few-shot relation learning, in AAAI, 2020. T. Gao, X. Han, R. Xie, Z. Liu, F. Lin, L. Lin, and M. Sun. paper code
Associative alignment for few-shot image classification, in ECCV, 2020. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper code
Information maximization for few-shot learning, in NeurIPS, 2020. M. Boudiaf, I. Ziko, J. Rony, J. Dolz, P. Piantanida, and I. B. Ayed. paper code
Self-training for few-shot transfer across extreme task differences, in ICLR, 2021. C. P. Phoo, and B. Hariharan. paper
Free lunch for few-shot learning: Distribution calibration, in ICLR, 2021. S. Yang, L. Liu, and M. Xu. paper code
Parameterless transductive feature re-representation for few-shot learning, in ICML, 2021. W. Cui, and Y. Guo;. paper
Learning intact features by erasing-inpainting for few-shot classification, in AAAI, 2021. J. Li, Z. Wang, and X. Hu. paper
Variational feature disentangling for fine-grained few-shot classification, in ICCV, 2021. J. Xu, H. Le, M. Huang, S. Athar, and D. Samaras. paper
Coarsely-labeled data for better few-shot transfer, in ICCV, 2021. C. P. Phoo, and B. Hariharan. paper
Pseudo-loss confidence metric for semi-supervised few-shot learning, in ICCV, 2021. K. Huang, J. Geng, W. Jiang, X. Deng, and Z. Xu. paper
Iterative label cleaning for transductive and semi-supervised few-shot learning, in ICCV, 2021. M. Lazarou, T. Stathaki, and Y. Avrithis. paper
Meta two-sample testing: Learning kernels for testing with limited data, in NeurIPS, 2021. F. Liu, W. Xu, J. Lu, and D. J. Sutherland. paper
Dynamic distillation network for cross-domain few-shot recognition with unlabeled data, in NeurIPS, 2021. A. Islam, C.-F. Chen, R. Panda, L. Karlinsky, R. Feris, and R. Radke. paper
Towards better understanding and better generalization of low-shot classification in histology images with contrastive learning, in ICLR, 2022. J. Yang, H. Chen, J. Yan, X. Chen, and J. Yao. paper code
FlipDA: Effective and robust data augmentation for few-shot learning, in ACL, 2022. J. Zhou, Y. Zheng, J. Tang, L. Jian, and Z. Yang. paper code
PromDA: Prompt-based data augmentation for low-resource NLU tasks, in ACL, 2022. Y. Wang, C. Xu, Q. Sun, H. Hu, C. Tao, X. Geng, and D. Jiang. paper code
N-shot learning for augmenting task-oriented dialogue state tracking, in Findings of ACL, 2022. I. T. Aksu, Z. Liu, M. Kan, and N. F. Chen. paper
Generating representative samples for few-shot classification, in CVPR, 2022. J. Xu, and H. Le. paper code
Semi-supervised few-shot learning via multi-factor clustering, in CVPR, 2022. J. Ling, L. Liao, M. Yang, and J. Shuai. paper
Information augmentation for few-shot node classification, in IJCAI, 2022. Z. Wu, P. Zhou, G. Wen, Y. Wan, J. Ma, D. Cheng, and X. Zhu. paper
Improving task-specific generalization in few-shot learning via adaptive vicinal risk minimization, in NeurIPS, 2022. L.-K. Huang, and Y. Wei. paper
An embarrassingly simple approach to semi-supervised few-shot learning, in NeurIPS, 2022. X.-S. Wei, H.-Y. Xu, F. Zhang, Y. Peng, and W. Zhou. paper
FeLMi : Few shot learning with hard mixup, in NeurIPS, 2022. A. Roy, A. Shah, K. Shah, P. Dhar, A. Cherian, and R. Chellappa. paper code
Understanding cross-domain few-shot learning based on domain similarity and few-shot difficulty, in NeurIPS, 2022. J. Oh, S. Kim, N. Ho, J.-H. Kim, H. Song, and S.-Y. Yun. paper code
Label hallucination for few-shot classification, in AAAI, 2022. Y. Jian, and L. Torresani. paper code
STUNT: Few-shot tabular learning with self-generated tasks from unlabeled tables, in ICLR, 2023. J. Nam, J. Tack, K. Lee, H. Lee, and J. Shin. paper code
Unsupervised meta-learning via few-shot pseudo-supervised contrastive learning, in ICLR, 2023. H. Jang, H. Lee, and J. Shin. paper code
Progressive mix-up for few-shot supervised multi-source domain transfer, in ICLR, 2023. R. Zhu, R. Zhu, X. Yu, and S. Li. paper code
Cross-level distillation and feature denoising for cross-domain few-shot classification, in ICLR, 2023. H. ZHENG, R. Wang, J. Liu, and A. Kanezaki. paper code
Tuning language models as training data generators for augmentation-enhanced few-shot learning, in ICML, 2023. Y. Meng, M. Michalski, J. Huang, Y. Zhang, T. F. Abdelzaher, and J. Han. paper code
Self-evolution learning for mixup: Enhance data augmentation on few-shot text classification tasks, in EMNLP, 2023. H. Zheng, Q. Zhong, L. Ding, Z. Tian, X. Niu, C. Wang, D. Li, and D. Tao. paper
Effective data augmentation with diffusion models, in ICLR, 2024. B. Trabucco, K. Doherty, M. A. Gurinas, and R. Salakhutdinov. paper code

Model

Multitask Learning

Multi-task transfer methods to improve one-shot learning for multimedia event detection, in BMVC, 2015. W. Yan, J. Yap, and G. Mori. paper
Label efficient learning of transferable representations across domains and tasks, in NeurIPS, 2017. Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei. paper
Few-shot adversarial domain adaptation, in NeurIPS, 2017. S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto. paper
One-shot unsupervised cross domain translation, in NeurIPS, 2018. S. Benaim and L. Wolf. paper
Multi-content GAN for few-shot font style transfer, in CVPR, 2018. S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman, and T. Darrell. paper code
Feature space transfer for data augmentation, in CVPR, 2018. B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos. paper
Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data, in ECCV, 2018. Y. Zhang, H. Tang, and K. Jia. paper
Few-shot charge prediction with discriminative legal attributes, in COLING, 2018. Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun. paper
Boosting few-shot visual learning with self-supervision, in ICCV, 2019. S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord. paper
When does self-supervision improve few-shot learning?, in ECCV, 2020. J. Su, S. Maji, and B. Hariharan. paper
Pareto self-supervised training for few-shot learning, in CVPR, 2021. Z. Chen, J. Ge, H. Zhan, S. Huang, and D. Wang. paper
Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation, in ICML, 2021. H. Wang, H. Zhao, and B. Li;. paper code
Task-level self-supervision for cross-domain few-shot learning, in AAAI, 2022. W. Yuan, Z. Zhang, C. Wang, H. Song, Y. Xie, and L. Ma. paper
Improving few-shot generalization by exploring and exploiting auxiliary data, in NeurIPS, 2023. A. Albalak, C. Raffel, and W. Y. Wang. paper code

Embedding/Metric Learning

Object classification from a single example utilizing class relevance metrics, in NeurIPS, 2005. M. Fink. paper
Optimizing one-shot recognition with micro-set learning, in CVPR, 2010. K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert. paper
Siamese neural networks for one-shot image recognition, ICML deep learning workshop, 2015. G. Koch, R. Zemel, and R. Salakhutdinov. paper
Matching networks for one shot learning, in NeurIPS, 2016. O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. paper
Learning feed-forward one-shot learners, in NeurIPS, 2016. L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi. paper
Few-shot learning through an information retrieval lens, in NeurIPS, 2017. E. Triantafillou, R. Zemel, and R. Urtasun. paper
Prototypical networks for few-shot learning, in NeurIPS, 2017. J. Snell, K. Swersky, and R. S. Zemel. paper code
Attentive recurrent comparators, in ICML, 2017. P. Shyam, S. Gupta, and A. Dukkipati. paper
Learning algorithms for active learning, in ICML, 2017. P. Bachman, A. Sordoni, and A. Trischler. paper
Active one-shot learning, arXiv preprint, 2017. M. Woodward and C. Finn. paper
Structured set matching networks for one-shot part labeling, in CVPR, 2018. J. Choi, J. Krishnamurthy, A. Kembhavi, and A. Farhadi. paper
Low-shot learning from imaginary data, in CVPR, 2018. Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan. paper
Learning to compare: Relation network for few-shot learning, in CVPR, 2018. F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales. paper code
Dynamic conditional networks for few-shot learning, in ECCV, 2018. F. Zhao, J. Zhao, S. Yan, and J. Feng. paper code
TADAM: Task dependent adaptive metric for improved few-shot learning, in NeurIPS, 2018. B. Oreshkin, P. R. López, and A. Lacoste. paper
Meta-learning for semi-supervised few-shot classification, in ICLR, 2018. M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenen- baum, H. Larochelle, and R. S. Zemel. paper code
Few-shot learning with graph neural networks, in ICLR, 2018. V. G. Satorras and J. B. Estrach. paper code
A simple neural attentive meta-learner, in ICLR, 2018. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel. paper
Meta-learning with differentiable closed-form solvers, in ICLR, 2019. L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi. paper
Learning to propagate labels: Transductive propagation network for few-shot learning, in ICLR, 2019. Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. Hwang, and Y. Yang. paper code
Multi-level matching and aggregation network for few-shot relation classification, in ACL, 2019. Z.-X. Ye, and Z.-H. Ling. paper
Induction networks for few-shot text classification, in EMNLP-IJCNLP, 2019. R. Geng, B. Li, Y. Li, X. Zhu, P. Jian, and J. Sun. paper
Hierarchical attention prototypical networks for few-shot text classification, in EMNLP-IJCNLP, 2019. S. Sun, Q. Sun, K. Zhou, and T. Lv. paper
Cross attention network for few-shot classification, in NeurIPS, 2019. R. Hou, H. Chang, B. Ma, S. Shan, and X. Chen. paper
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes, in NeurIPS, 2019. J. Requeima, J. Gordon, J. Bronskill, S. Nowozin, and R. E. Turner. paper code
Hybrid attention-based prototypical networks for noisy few-shot relation classification, in AAAI, 2019. T. Gao, X. Han, Z. Liu, and M. Sun. paper code
Attention-based multi-context guiding for few-shot semantic segmentation, in AAAI, 2019. T. Hu, P. Yang, C. Zhang, G. Yu, Y. Mu and C. G. M. Snoek. paper
Distribution consistency based covariance metric networks for few-shot learning, in AAAI, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao and J. Luo. paper
A dual attention network with semantic embedding for few-shot learning, in AAAI, 2019. S. Yan, S. Zhang, and X. He. paper
TapNet: Neural network augmented with task-adaptive projection for few-shot learning, in ICML, 2019. S. W. Yoon, J. Seo, and J. Moon. paper
Prototype propagation networks (PPN) for weakly-supervised few-shot learning on category graph, in IJCAI, 2019. L. Liu, T. Zhou, G. Long, J. Jiang, L. Yao, C. Zhang. paper code
Collect and select: Semantic alignment metric learning for few-shot learning, in ICCV, 2019. F. Hao, F. He, J. Cheng, L. Wang, J. Cao, and D. Tao. paper
Transductive episodic-wise adaptive metric for few-shot learning, in ICCV, 2019. L. Qiao, Y. Shi, J. Li, Y. Wang, T. Huang, and Y. Tian. paper
Few-shot learning with embedded class models and shot-free meta training, in ICCV, 2019. A. Ravichandran, R. Bhotika, and S. Soatto. paper
PARN: Position-aware relation networks for few-shot learning, in ICCV, 2019. Z. Wu, Y. Li, L. Guo, and K. Jia. paper
PANet: Few-shot image semantic segmentation with prototype alignment, in ICCV, 2019. K. Wang, J. H. Liew, Y. Zou, D. Zhou, and J. Feng. paper code
RepMet: Representative-based metric learning for classification and few-shot object detection, in CVPR, 2019. L. Karlinsky, J. Shtok, S. Harary, E. Schwartz, A. Aides, R. Feris, R. Giryes, and A. M. Bronstein. paper code
Edge-labeling graph neural network for few-shot learning, in CVPR, 2019. J. Kim, T. Kim, S. Kim, and C. D. Yoo. paper
Finding task-relevant features for few-shot learning by category traversal, in CVPR, 2019. H. Li, D. Eigen, S. Dodge, M. Zeiler, and X. Wang. paper code
Revisiting local descriptor based image-to-class measure for few-shot learning, in CVPR, 2019. W. Li, L. Wang, J. Xu, J. Huo, Y. Gao, and J. Luo. paper code
TAFE-Net: Task-aware feature embeddings for low shot learning, in CVPR, 2019. X. Wang, F. Yu, R. Wang, T. Darrell, and J. E. Gonzalez. paper code
Improved few-shot visual classification, in CVPR, 2020. P. Bateni, R. Goyal, V. Masrani, F. Wood, and L. Sigal. paper
Boosting few-shot learning with adaptive margin loss, in CVPR, 2020. A. Li, W. Huang, X. Lan, J. Feng, Z. Li, and L. Wang. paper
Adaptive subspaces for few-shot learning, in CVPR, 2020. C. Simon, P. Koniusz, R. Nock, and M. Harandi. paper
DPGN: Distribution propagation graph network for few-shot learning, in CVPR, 2020. L. Yang, L. Li, Z. Zhang, X. Zhou, E. Zhou, and Y. Liu. paper
Few-shot learning via embedding adaptation with set-to-set functions, in CVPR, 2020. H.-J. Ye, H. Hu, D.-C. Zhan, and F. Sha. paper code
DeepEMD: Few-shot image classification with differentiable earth mover's distance and structured classifiers, in CVPR, 2020. C. Zhang, Y. Cai, G. Lin, and C. Shen. paper code
Few-shot text classification with distributional signatures, in ICLR, 2020. Y. Bao, M. Wu, S. Chang, and R. Barzilay. paper code
Learning task-aware local representations for few-shot learning, in IJCAI, 2020. C. Dong, W. Li, J. Huo, Z. Gu, and Y. Gao. paper
SimPropNet: Improved similarity propagation for few-shot image segmentation, in IJCAI, 2020. S. Gairola, M. Hemani, A. Chopra, and B. Krishnamurthy. paper
Asymmetric distribution measure for few-shot learning, in IJCAI, 2020. W. Li, L. Wang, J. Huo, Y. Shi, Y. Gao, and J. Luo. paper
Transductive relation-propagation network for few-shot learning, in IJCAI, 2020. Y. Ma, S. Bai, S. An, W. Liu, A. Liu, X. Zhen, and X. Liu. paper
Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings, in IJCAI, 2020. M. Siam, N. Doraiswamy, B. N. Oreshkin, H. Yao, and M. Jägersand. paper
Few-shot learning on graphs via super-classes based on graph spectral measures, in ICLR, 2020. J. Chauhan, D. Nathani, and M. Kaul. paper
SGAP-Net: Semantic-guided attentive prototypes network for few-shot human-object interaction recognition, in AAAI, 2020. Z. Ji, X. Liu, Y. Pang, and X. Li. paper
One-shot image classification by learning to restore prototypes, in AAAI, 2020. W. Xue, and W. Wang. paper
Negative margin matters: Understanding margin in few-shot classification, in ECCV, 2020. B. Liu, Y. Cao, Y. Lin, Q. Li, Z. Zhang, M. Long, and H. Hu. paper code
Prototype rectification for few-shot learning, in ECCV, 2020. J. Liu, L. Song, and Y. Qin. paper
Rethinking few-shot image classification: A good embedding is all you need?, in ECCV, 2020. Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola. paper code
SEN: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks, in ECCV, 2020. V. N. Nguyen, S. Løkse, K. Wickstrøm, M. Kampffmeyer, D. Roverso, and R. Jenssen. paper
TAFSSL: Task-adaptive feature sub-space learning for few-shot classification, in ECCV, 2020. M. Lichtenstein, P. Sattigeri, R. Feris, R. Giryes, and L. Karlinsky. paper
Attentive prototype few-shot learning with capsule network-based embedding, in ECCV, 2020. F. Wu, J. S.Smith, W. Lu, C. Pang, and B. Zhang. paper
Embedding propagation: Smoother manifold for few-shot classification, in ECCV, 2020. P. Rodríguez, I. Laradji, A. Drouin, and A. Lacoste. paper code
Laplacian regularized few-shot learning, in ICML, 2020. I. M. Ziko, J. Dolz, E. Granger, and I. B. Ayed. paper code
TAdaNet: Task-adaptive network for graph-enriched meta-learning, in KDD, 2020. Q. Suo, i. Chou, W. Zhong, and A. Zhang. paper
Concept learners for few-shot learning, in ICLR, 2021. K. Cao, M. Brbic, and J. Leskovec. paper
Reinforced attention for few-shot learning and beyond, in CVPR, 2021. J. Hong, P. Fang, W. Li, T. Zhang, C. Simon, M. Harandi, and L. Petersson. paper
Mutual CRF-GNN for few-shot learning, in CVPR, 2021. S. Tang, D. Chen, L. Bai, K. Liu, Y. Ge, and W. Ouyang. paper
Few-shot classification with feature map reconstruction networks, in CVPR, 2021. D. Wertheimer, L. Tang, and B. Hariharan. paper code
ECKPN: Explicit class knowledge propagation network for transductive few-shot learning, in CVPR, 2021. C. Chen, X. Yang, C. Xu, X. Huang, and Z. Ma. paper
Exploring complementary strengths of invariant and equivariant representations for few-shot learning, in CVPR, 2021. M. N. Rizve, S. Khan, F. S. Khan, and M. Shah. paper
Rethinking class relations: Absolute-relative supervised and unsupervised few-shot learning, in CVPR, 2021. H. Zhang, P. Koniusz, S. Jian, H. Li, and P. H. S. Torr. paper
Unsupervised embedding adaptation via early-stage feature reconstruction for few-shot classification, in ICML, 2021. D. H. Lee, and S. Chung. paper code
Learning a few-shot embedding model with contrastive learning, in AAAI, 2021. C. Liu, Y. Fu, C. Xu, S. Yang, J. Li, C. Wang, and L. Zhang. paper
Looking wider for better adaptive representation in few-shot learning, in AAAI, 2021. J. Zhao, Y. Yang, X. Lin, J. Yang, and L. He. paper
Tailoring embedding function to heterogeneous few-shot tasks by global and local feature adaptors, in AAAI, 2021. S. Lu, H. Ye, and D.-C. Zhan. paper
Knowledge guided metric learning for few-shot text classification, in NAACL-HLT, 2021. D. Sui, Y. Chen, B. Mao, D. Qiu, K. Liu, and J. Zhao. paper
Mixture-based feature space learning for few-shot image classification, in ICCV, 2021. A. Afrasiyabi, J. Lalonde, and C. Gagné. paper
Z-score normalization, hubness, and few-shot learning, in ICCV, 2021. N. Fei, Y. Gao, Z. Lu, and T. Xiang. paper
Relational embedding for few-shot classification, in ICCV, 2021. D. Kang, H. Kwon, J. Min, and M. Cho. paper code
Transductive few-shot classification on the oblique manifold, in ICCV, 2021. G. Qi, H. Yu, Z. Lu, and S. Li. paper code
Curvature generation in curved spaces for few-shot learning, in ICCV, 2021. Z. Gao, Y. Wu, Y. Jia, and M. Harandi. paper
On episodes, prototypical networks, and few-shot learning, in NeurIPS, 2021. S. Laenen, and L. Bertinetto. paper
Few-shot learning as cluster-induced voronoi diagrams: A geometric approach, in ICLR, 2022. C. Ma, Z. Huang, M. Gao, and J. Xu. paper code
Few-shot learning with siamese networks and label tuning, in ACL, 2022. T. Müller, G. Pérez-Torró, and M. Franco-Salvador. paper code
Learning to affiliate: Mutual centralized learning for few-shot classification, in CVPR, 2022. Y. Liu, W. Zhang, C. Xiang, T. Zheng, D. Cai, and X. He. paper
Matching feature sets for few-shot image classification, in CVPR, 2022. A. Afrasiyabi, H. Larochelle, J. Lalonde, and C. Gagné. paper code
Joint distribution matters: Deep Brownian distance covariance for few-shot classification, in CVPR, 2022. J. Xie, F. Long, J. Lv, Q. Wang, and P. Li. paper
CAD: Co-adapting discriminative features for improved few-shot classification, in CVPR, 2022. P. Chikontwe, S. Kim, and S. H. Park. paper
Ranking distance calibration for cross-domain few-shot learning, in CVPR, 2022. P. Li, S. Gong, C. Wang, and Y. Fu. paper
EASE: Unsupervised discriminant subspace learning for transductive few-shot learning, in CVPR, 2022. H. Zhu, and P. Koniusz. paper code
Cross-domain few-shot learning with task-specific adapters, in CVPR, 2022. W. Li, X. Liu, and H. Bilen. paper code
Hyperbolic knowledge transfer with class hierarchy for few-shot learning, in IJCAI, 2022. B. Zhang, H. Jiang, S. Feng, X. Li, Y. Ye, and R. Ye. paper
Better embedding and more shots for few-shot learning, in IJCAI, 2022. Z. Chi, Z. Wang, M. Yang, W. Guo, and X. Xu. paper
A closer look at prototype classifier for few-shot image classification, in NeurIPS, 2022. M. Hou, and I. Sato. paper
Rethinking generalization in few-shot classification, in NeurIPS, 2022. M. Hiller, R. Ma, M. Harandi, and T. Drummond. paper code
DMN4: Few-shot learning via discriminative mutual nearest neighbor neural network, in AAAI, 2022. Y. Liu, T. Zheng, J. Song, D. Cai, and X. He. paper
Hybrid graph neural networks for few-shot learning, in AAAI, 2022. T. Yu, S. He, Y.-Z. Song, and T. Xiang. paper code
Adaptive poincaré point to set distance for few-shot classification, in AAAI, 2022. R. Ma, P. Fang, T. Drummond, and M. Harandi. paper
Hubs and hyperspheres: Reducing hubness and improving transductive few-shot learning with hyperspherical embeddings, in CVPR, 2023. D. J. Trosten, R. Chakraborty, S. Løkse, K. K. Wickstrøm, R. Jenssen, and M. C. Kampffmeyer. paper code
Revisiting prototypical network for cross domain few-shot learning, in CVPR, 2023. F. Zhou, P. Wang, L. Zhang, W. Wei, and Y. Zhang. paper code
Transductive few-shot learning with prototype-based label propagation by iterative graph refinement, in CVPR, 2023. H. Zhu, and P. Koniusz. paper code
Few-shot classification via ensemble learning with multi-order statistics, in IJCAI, 2023. S. Yang, F. Liu, D. Chen, and J. Zhou. paper
Few-sample feature selection via feature manifold learning, in ICML, 2023. D. Cohen, T. Shnitzer, Y. Kluger, and R. Talmon. paper code
Interval bound interpolation for few-shot learning with few tasks, in ICML, 2023. S. Datta, S. S. Mullick, A. Chakrabarty, and S. Das. paper code
A closer look at few-shot classification again, in ICML, 2023. X. Luo, H. Wu, J. Zhang, L. Gao, J. Xu, and J. Song. paper code
TART: Improved few-shot text classification using task-adaptive reference transformation, in ACL, 2023. S. Lei, X. Zhang, J. He, F. Chen, and C.-T. Lu. paper code
Class-aware patch embedding adaptation for few-shot image classification, in ICCV, 2023. F. Hao, F. He, L. Liu, F. Wu, D. Tao, and J. Cheng. paper code
Frequency guidance matters in few-shot learning, in ICCV, 2023. H. Cheng, S. Yang, J. T. Zhou, L. Guo, and B. Wen. paper
Prototypes-oriented transductive few-shot learning with conditional transport, in ICCV, 2023. L. Tian, J. Feng, X. Chai, W. Chen, L. Wang, X. Liu, and B. Chen. paper code
Understanding few-shot learning: Measuring task relatedness and adaptation difficulty via attributes, in NeurIPS, 2023. M. Hu, H. Chang, Z. Guo, B. Ma, S. Shan, and X. Chen. paper code
DiffKendall: A novel approach for few-shot learning with differentiable Kendall's rank correlation, in NeurIPS, 2023. K. Zheng, H. Zhang, and W. Huang. paper
Compositional prototypical networks for few-shot classification, in AAAI, 2023. Q. Lyu, and W. Wang. paper code
FoPro: Few-shot guided robust webly-supervised prototypical learning, in AAAI, 2023. Y. Qin, X. Chen, C. Chen, Y. Shen, B. Ren, Y. Gu, J. Yang, and C. Shen. paper code
SpatialFormer: Semantic and target aware attentions for few-shot learning, in AAAI, 2023. J. Lai, S. Yang, W. Wu, T. Wu, G. Jiang, X. Wang, J. Liu, B.-B. Gao, W. Zhang, Y. Xie, and C. Wang. paper
RankDNN: Learning to rank for few-shot learning, in AAAI, 2023. Q. Guo, H. Gong, X. Wei, Y. Fu, Y. Yu, W. Zhang, and W. Ge. paper code
Boosting few-shot text classification via distribution estimation, in AAAI, 2023. H. Liu, F. Zhang, X. Zhang, S. Zhao, F. Ma, X.-M. Wu, H. Chen, H. Yu, and X. Zhang. paper
Feature distribution fitting with direction-driven weighting for few-shot images classification, in AAAI, 2023. X. Wei, W. Du, H. Wan, and W. Min. paper
Exploring tuning characteristics of ventral stream's neurons for few-shot image classification, in AAAI, 2023. L. Dong, W. Zhai, and Z.-J. Zha. paper
RAPL: A relation-aware prototype learning approach for few-shot document-level relation extraction, in EMNLP, 2023. S. Meng, X. Hu, A. Liu, S. Li, F. Ma, Y. Yang, and L. Wen. paper code
BECLR: Batch enhanced contrastive few-shot learning, in ICLR, 2024. S. Poulakakis-Daktylidis, and H. Jamali-Rad. paper code

Learning with External Memory

Meta-learning with memory-augmented neural networks, in ICML, 2016. A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. paper
Few-shot object recognition from machine-labeled web images, in CVPR, 2017. Z. Xu, L. Zhu, and Y. Yang. paper
Learning to remember rare events, in ICLR, 2017. Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio. paper
Meta networks, in ICML, 2017. T. Munkhdalai and H. Yu. paper
Memory matching networks for one-shot image recognition, in CVPR, 2018. Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei. paper
Compound memory networks for few-shot video classification, in ECCV, 2018. L. Zhu and Y. Yang. paper
Memory, show the way: Memory based few shot word representation learning, in EMNLP, 2018. J. Sun, S. Wang, and C. Zong. paper
Rapid adaptation with conditionally shifted neurons, in ICML, 2018. T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler. paper
Adaptive posterior learning: Few-shot learning with a surprise-based memory module, in ICLR, 2019. T. Ramalho and M. Garnelo. paper code
Coloring with limited data: Few-shot colorization via memory augmented networks, in CVPR, 2019. S. Yoo, H. Bahng, S. Chung, J. Lee, J. Chang, and J. Choo. paper
ACMM: Aligned cross-modal memory for few-shot image and sentence matching, in ICCV, 2019. Y. Huang, and L. Wang. paper
Dynamic memory induction networks for few-shot text classification, in ACL, 2020. R. Geng, B. Li, Y. Li, J. Sun, and X. Zhu. paper
Few-shot visual learning with contextual memory and fine-grained calibration, in IJCAI, 2020. Y. Ma, W. Liu, S. Bai, Q. Zhang, A. Liu, W. Chen, and X. Liu. paper
Learn from concepts: Towards the purified memory for few-shot learning, in IJCAI, 2021. X. Liu, X. Tian, S. Lin, Y. Qu, L. Ma, W. Yuan, Z. Zhang, and Y. Xie. paper
Prototype memory and attention mechanisms for few shot image generation, in ICLR, 2022. T. Li, Z. Li, A. Luo, H. Rockwell, A. B. Farimani, and T. S. Lee. paper code
Hierarchical variational memory for few-shot learning across domains, in ICLR, 2022. Y. Du, X. Zhen, L. Shao, and C. G. M. Snoek. paper code
Remember the difference: Cross-domain few-shot semantic segmentation via meta-memory transfer, in CVPR, 2022. W. Wang, L. Duan, Y. Wang, Q. En, J. Fan, and Z. Zhang. paper
Consistent prototype learning for few-shot continual relation extraction, in ACL, 2023. X. Chen, H. Wu, and X. Shi. paper code
Few-shot generation via recalling brain-inspired episodic-semantic memory, in NeurIPS, 2023. Z. Duan, L. Zhiyi, C. Wang, B. Chen, B. An, and M. Zhou. paper

Generative Modeling

One-shot learning of object categories, TPAMI, 2006. L. Fei-Fei, R. Fergus, and P. Perona. paper
Learning to learn with compound HD models, in NeurIPS, 2011. A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov. paper
One-shot learning with a hierarchical nonparametric bayesian model, in ICML Workshop on Unsupervised and Transfer Learning, 2012. R. Salakhutdinov, J. Tenenbaum, and A. Torralba. paper
Human-level concept learning through probabilistic program induction, Science, 2015. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper
One-shot generalization in deep generative models, in ICML, 2016. D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra. paper
One-shot video object segmentation, in CVPR, 2017. S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool. paper
Towards a neural statistician, in ICLR, 2017. H. Edwards and A. Storkey. paper
Extending a parser to distant domains using a few dozen partially annotated examples, in ACL, 2018. V. Joshi, M. Peters, and M. Hopkins. paper
MetaGAN: An adversarial approach to few-shot learning, in NeurIPS, 2018. R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song. paper
Few-shot autoregressive density estimation: Towards learning to learn distributions, in ICLR, 2018. S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas. paper
The variational homoencoder: Learning to learn high capacity generative models from few examples, in UAI, 2018. L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum. paper
Meta-learning probabilistic inference for prediction, in ICLR, 2019. J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner. paper
Variational prototyping-encoder: One-shot learning with prototypical images, in CVPR, 2019. J. Kim, T.-H. Oh, S. Lee, F. Pan, and I. S. Kweon. paper code
Variational few-shot learning, in ICCV, 2019. J. Zhang, C. Zhao, B. Ni, M. Xu, and X. Yang. paper
Infinite mixture prototypes for few-shot learning, in ICML, 2019. K. Allen, E. Shelhamer, H. Shin, and J. Tenenbaum. paper
Dual variational generation for low shot heterogeneous face recognition, in NeurIPS, 2019. C. Fu, X. Wu, Y. Hu, H. Huang, and R. He. paper
Bayesian meta sampling for fast uncertainty adaptation, in ICLR, 2020. Z. Wang, Y. Zhao, P. Yu, R. Zhang, and C. Chen. paper
Empirical Bayes transductive meta-learning with synthetic gradients, in ICLR, 2020. S. X. Hu, P. G. Moreno, Y. Xiao, X. Shen, G. Obozinski, N. D. Lawrence, and A. C. Damianou. paper
Few-shot relation extraction via bayesian meta-learning on relation graphs, in ICML, 2020. M. Qu, T. Gao, L. A. C. Xhonneux, and J. Tang. paper code
Interventional few-shot learning, in NeurIPS, 2020. Z. Yue, H. Zhang, Q. Sun, and X. Hua. paper code
Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes, in ICLR, 2021. J. Snell, and R. Zemel. paper
Few-shot Bayesian optimization with deep kernel surrogates, in ICLR, 2021. M. Wistuba, and J. Grabocka. paper
A hierarchical transformation-discriminating generative model for few shot anomaly detection, in ICCV, 2021. S. Sheynin, S. Benaim, and L. Wolf. paper
Reinforced few-shot acquisition function learning for Bayesian optimization, in NeurIPS, 2021. B. Hsieh, P. Hsieh, and X. Liu. paper
GanOrCon: Are generative models useful for few-shot segmentation?, in CVPR, 2022. O. Saha, Z. Cheng, and S. Maji. paper
Few shot generative model adaption via relaxed spatial structural alignment, in CVPR, 2022. J. Xiao, L. Li, C. Wang, Z. Zha, and Q. Huang. paper
SCHA-VAE: Hierarchical context aggregation for few-shot generation, in ICML, 2022. G. Giannone, and O. Winther. paper code
Diversity vs. Recognizability: Human-like generalization in one-shot generative models, in NeurIPS, 2022. V. Boutin, L. Singhal, X. Thomas, and T. Serre. paper code
Generalized one-shot domain adaptation of generative adversarial networks, in NeurIPS, 2022. Z. Zhang, Y. Liu, C. Han, T. Guo, T. Yao, and T. Mei. paper code
Towards diverse and faithful one-shot adaption of generative adversarial networks, in NeurIPS, 2022. Y. Zhang, m. Yao, Y. Wei, Z. Ji, J. Bai, and W. Zuo. paper code
Few-shot cross-domain image generation via inference-time latent-code learning, in ICLR, 2023. A. K. Mondal, P. Tiwary, P. Singla, and P. AP. paper code
Adaptive IMLE for few-shot pretraining-free generative modelling, in ICML, 2023. M. Aghabozorgi, S. Peng, and K. Li. paper code
Diversity-enhancing generative network for few-shot hypothesis adaptation, in ICML, 2023. R. Dong, F. Liu, H. Chi, T. Liu, M. Gong, G. Niu, M. Sugiyama, and B. Han. paper
MetaModulation: Learning variational feature hierarchies for few-shot learning with fewer tasks, in ICML, 2023. W. Sun, Y. Du, X. Zhen, F. Wang, L. Wang, and C. G. M. Snoek. paper code
Revisiting logistic-softmax likelihood in bayesian meta-learning for few-shot classification, in NeurIPS, 2023. T. Ke, H. Cao, Z. Ling, and F. Zhou. paper code
Human-like few-shot learning via bayesian reasoning over natural language, in NeurIPS, 2023. and K. Ellis. paper code
CMVAE: Causal meta VAE for unsupervised meta-learning, in AAAI, 2023. G. Qi, and H. Yu. paper code
Progressive few-shot adaptation of generative model with align-free spatial correlation, in AAAI, 2023. J. Moon, H. Kim, and J.-P. Heo. paper

Algorithm

Refining Existing Parameters

Cross-generalization: Learning novel classes from a single example by feature replacement, in CVPR, 2005. E. Bart and S. Ullman. paper
One-shot adaptation of supervised deep convolutional models, in ICLR, 2013. J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell. paper
Learning to learn: Model regression networks for easy small sample learning, in ECCV, 2016. Y.-X. Wang and M. Hebert. paper
Learning from small sample sets by combining unsupervised meta-training with CNNs, in NeurIPS, 2016. Y.-X. Wang and M. Hebert. paper
Efficient k-shot learning with regularized deep networks, in AAAI, 2018. D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani. paper
CLEAR: Cumulative learning for one-shot one-class image recognition, in CVPR, 2018. J. Kozerawski and M. Turk. paper
Learning structure and strength of CNN filters for small sample size training, in CVPR, 2018. R. Keshari, M. Vatsa, R. Singh, and A. Noore. paper
Dynamic few-shot visual learning without forgetting, in CVPR, 2018. S. Gidaris and N. Komodakis. paper code
Low-shot learning with imprinted weights, in CVPR, 2018. H. Qi, M. Brown, and D. G. Lowe. paper
Neural voice cloning with a few samples, in NeurIPS, 2018. S. Arik, J. Chen, K. Peng, W. Ping, and Y. Zhou. paper
Text classification with few examples using controlled generalization, in NAACL-HLT, 2019. A. Mahabal, J. Baldridge, B. K. Ayan, V. Perot, and D. Roth. paper
Low shot box correction for weakly supervised object detection, in IJCAI, 2019. T. Pan, B. Wang, G. Ding, J. Han, and J. Yong. paper
Diversity with cooperation: Ensemble methods for few-shot classification, in ICCV, 2019. N. Dvornik, C. Schmid, and J. Mairal. paper
Few-shot image recognition with knowledge transfer, in ICCV, 2019. Z. Peng, Z. Li, J. Zhang, Y. Li, G.-J. Qi, and J. Tang. paper
Generating classification weights with gnn denoising autoencoders for few-shot learning, in CVPR, 2019. S. Gidaris, and N. Komodakis. paper code
Dense classification and implanting for few-shot learning, in CVPR, 2019. Y. Lifchitz, Y. Avrithis, S. Picard, and A. Bursuc. paper
Few-shot adaptive faster R-CNN, in CVPR, 2019. T. Wang, X. Zhang, L. Yuan, and J. Feng. paper
TransMatch: A transfer-learning scheme for semi-supervised few-shot learning, in CVPR, 2020. Z. Yu, L. Chen, Z. Cheng, and J. Luo. paper
Learning to select base classes for few-shot classification, in CVPR, 2020. L. Zhou, P. Cui, X. Jia, S. Yang, and Q. Tian. paper
Few-shot NLG with pre-trained language model, in ACL, 2020. Z. Chen, H. Eavani, W. Chen, Y. Liu, and W. Y. Wang. paper code
Span-ConveRT: Few-shot span extraction for dialog with pretrained conversational representations, in ACL, 2020. S. Coope, T. Farghly, D. Gerz, I. Vulic, and M. Henderson. paper
Structural supervision improves few-shot learning and syntactic generalization in neural language models, in EMNLP, 2020. E. Wilcox, P. Qian, R. Futrell, R. Kohita, R. Levy, and M. Ballesteros. paper code
A baseline for few-shot image classification, in ICLR, 2020. G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto. paper
Cross-domain few-shot classification via learned feature-wise transformation, in ICLR, 2020. H. Tseng, H. Lee, J. Huang, and M. Yang. paper code
Graph few-shot learning via knowledge transfer, in AAAI, 2020. H. Yao, C. Zhang, Y. Wei, M. Jiang, S. Wang, J. Huang, N. V. Chawla, and Z. Li. paper
Context-Transformer: Tackling object confusion for few-shot detection, in AAAI, 2020. Z. Yang, Y. Wang, X. Chen, J. Liu, and Y. Qiao. paper
A broader study of cross-domain few-shot learning, in ECCV, 2020. Y. Guo, N. C. Codella, L. Karlinsky, J. V. Codella, J. R. Smith, K. Saenko, T. Rosing, and R. Feris. paper code
Selecting relevant features from a multi-domain representation for few-shot classification, in ECCV, 2020. N. Dvornik, C. Schmid, and J. Mairal. paper code
Prototype completion with primitive knowledge for few-shot learning, in CVPR, 2021. B. Zhang, X. Li, Y. Ye, Z. Huang, and L. Zhang. paper code
Partial is better than all: Revisiting fine-tuning strategy for few-shot learning, in AAAI, 2021. Z. Shen, Z. Liu, J. Qin, M. Savvides, and K.-T. Cheng. paper
PTN: A poisson transfer network for semi-supervised few-shot learning, in AAAI, 2021. H. Huang, J. Zhang, J. Zhang, Q. Wu, and C. Xu. paper
A universal representation transformer layer for few-shot image classification, in ICLR, 2021. L. Liu, W. L. Hamilton, G. Long, J. Jiang, and H. Larochelle. paper
Making pre-trained language models better few-shot learners, in ACL-IJCNLP, 2021. T. Gao, A. Fisch, and D. Chen. paper code
Self-supervised network evolution for few-shot classification, in IJCAI, 2021. X. Tang, Z. Teng, B. Zhang, and J. Fan. paper
Calibrate before use: Improving few-shot performance of language models, in ICML, 2021. Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh. paper code
Language models are few-shot learners, in NeurIPS, 2020. T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei. paper
It's not just size that matters: Small language models are also few-shot learners, in NAACL-HLT, 2021. T. Schick, and H. Schütze. paper code
Self-training improves pre-training for few-shot learning in task-oriented dialog systems, in EMNLP, 2021. F. Mi, W. Zhou, L. Kong, F. Cai, M. Huang, and B. Faltings. paper
Few-shot intent detection via contrastive pre-training and fine-tuning, in EMNLP, 2021. J. Zhang, T. Bui, S. Yoon, X. Chen, Z. Liu, C. Xia, Q. H. Tran, W. Chang, and P. S. Yu. paper code
Avoiding inference heuristics in few-shot prompt-based finetuning, in EMNLP, 2021. P. A. Utama, N. S. Moosavi, V. Sanh, and I. Gurevych. paper code
Constrained language models yield few-shot semantic parsers, in EMNLP, 2021. R. Shin, C. H. Lin, S. Thomson, C. Chen, S. Roy, E. A. Platanios, A. Pauls, D. Klein, J. Eisner, and B. V. Durme. paper code
Revisiting self-training for few-shot learning of language model, in EMNLP, 2021. Y. Chen, Y. Zhang, C. Zhang, G. Lee, R. Cheng, and H. Li. paper code
Language models are few-shot butlers, in EMNLP, 2021. V. Micheli, and F. Fleuret. paper code
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models, in EMNLP, 2021. R. Chada, and P. Natarajan. paper
TransPrompt: Towards an automatic transferable prompting framework for few-shot text classification, in EMNLP, 2021. C. Wang, J. Wang, M. Qiu, J. Huang, and M. Gao. paper
Meta distant transfer learning for pre-trained language models, in EMNLP, 2021. C. Wang, H. Pan, M. Qiu, J. Huang, F. Yang, and Y. Zhang. paper
STraTA: Self-training with task augmentation for better few-shot learning, in EMNLP, 2021. T. Vu, M. Luong, Q. V. Le, G. Simon, and M. Iyyer. paper code
Few-shot image classification: Just use a library of pre-trained feature extractors and a simple classifier, in ICCV, 2021. A. Chowdhury, M. Jiang, S. Chaudhuri, and C. Jermaine. paper code
On the importance of distractors for few-shot classification, in ICCV, 2021. R. Das, Y. Wang, and J. M. F. Moura. paper code
A multi-mode modulator for multi-domain few-shot classification, in ICCV, 2021. Y. Liu, J. Lee, L. Zhu, L. Chen, H. Shi, and Y. Yang. paper
Universal representation learning from multiple domains for few-shot classification, in ICCV, 2021. W. Li, X. Liu, and H. Bilen. paper code
Boosting the generalization capability in cross-domain few-shot learning via noise-enhanced supervised autoencoder, in ICCV, 2021. H. Liang, Q. Zhang, P. Dai, and J. Lu. paper
How fine-tuning allows for effective meta-learning, in NeurIPS, 2021. K. Chua, Q. Lei, and J. D. Lee. paper
Multimodal few-shot learning with frozen language models, in NeurIPS, 2021. M. Tsimpoukelli, J. Menick, S. Cabi, S. M. A. Eslami, O. Vinyals, and F. Hill. paper
Grad2Task: Improved few-shot text classification using gradients for task representation, in NeurIPS, 2021. J. Wang, K. Wang, F. Rudzicz, and M. Brudno. paper
True few-shot learning with language models, in NeurIPS, 2021. E. Perez, D. Kiela, and K. Cho. paper
POODLE: Improving few-shot learning via penalizing out-of-distribution samples, in NeurIPS, 2021. D. Le, K. Nguyen, Q. Tran, R. Nguyen, and B. Hua. paper
TOHAN: A one-step approach towards few-shot hypothesis adaptation, in NeurIPS, 2021. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, W. Cheung, and J. Kwok. paper
Task affinity with maximum bipartite matching in few-shot learning, in ICLR, 2022. C. P. Le, J. Dong, M. Soltani, and V. Tarokh. paper
Differentiable prompt makes pre-trained language models better few-shot learners, in ICLR, 2022. N. Zhang, L. Li, X. Chen, S. Deng, Z. Bi, C. Tan, F. Huang, and H. Chen. paper code
ConFeSS: A framework for single source cross-domain few-shot learning, in ICLR, 2022. D. Das, S. Yun, and F. Porikli. paper
Switch to generalize: Domain-switch learning for cross-domain few-shot classification, in ICLR, 2022. Z. Hu, Y. Sun, and Y. Yang. paper
LM-BFF-MS: Improving few-shot fine-tuning of language models based on multiple soft demonstration memory, in ACL, 2022. E. Park, D. H. Jeon, S. Kim, I. Kang, and S. Na. paper code
Meta-learning via language model in-context tuning, in ACL, 2022. Y. Chen, R. Zhong, S. Zha, G. Karypis, and H. He. paper code
Few-shot tabular data enrichment using fine-tuned transformer architectures, in ACL, 2022. A. Harari, and G. Katz. paper
Noisy channel language model prompting for few-shot text classification, in ACL, 2022. S. Min, M. Lewis, H. Hajishirzi, and L. Zettlemoyer. paper code
Prompt for extraction? PAIE: Prompting argument interaction for event argument extraction, in ACL, 2022. Y. Ma, Z. Wang, Y. Cao, M. Li, M. Chen, K. Wang, and J. Shao. paper code
Are prompt-based models clueless?, in ACL, 2022. P. Kavumba, R. Takahashi, and Y. Oda. paper
Prototypical verbalizer for prompt-based few-shot tuning, in ACL, 2022. G. Cui, S. Hu, N. Ding, L. Huang, and Z. Liu. paper code
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity, in ACL, 2022. Y. Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp. paper
PPT: Pre-trained prompt tuning for few-shot learning, in ACL, 2022. Y. Gu, X. Han, Z. Liu, and M. Huang. paper code
ASCM: An answer space clustered prompting method without answer engineering, in Findings of ACL, 2022. Z. Wang, Y. Yang, Z. Xi, B. Ma, L. Wang, R. Dong, and A. Anwar. paper code
Exploiting language model prompts using similarity measures: A case study on the word-in-context task, in ACL, 2022. M. Tabasi, K. Rezaee, and M. T. Pilehvar. paper
P-Tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks, in ACL, 2022. X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang. paper
Cutting down on prompts and parameters: Simple few-shot learning with language models, in Findings of ACL, 2022. R. L. L. IV, I. Balazevic, E. Wallace, F. Petroni, S. Singh, and S. Riedel. paper code
Prompt-free and efficient few-shot learning with language models, in ACL, 2022. R. K. Mahabadi, L. Zettlemoyer, J. Henderson, L. Mathias, M. Saeidi, V. Stoyanov, and M. Yazdani. paper code
Pre-training to match for unified low-shot relation extraction, in ACL, 2022. F. Liu, H. Lin, X. Han, B. Cao, and L. Sun. paper code
Dual context-guided continuous prompt tuning for few-shot learning, in Findings of ACL, 2022. J. Zhou, L. Tian, H. Yu, Z. Xiao, H. Su, and J. Zhou. paper
Cluster & tune: Boost cold start performance in text classification, in ACL, 2022. E. Shnarch, A. Gera, A. Halfon, L. Dankin, L. Choshen, R. Aharonov, and N. Slonim. paper code
Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference, in CVPR, 2022. S. X. Hu, D. Li, J. Stühmer, M. Kim, and T. M. Hospedales. paper code
HyperTransformer: Model generation for supervised and semi-supervised few-shot learning, in ICML, 2022. A. Zhmoginov, M. Sandler, and M. Vladymyrov. paper code
Prompting ELECTRA: Few-shot learning with discriminative pre-trained models, in EMNLP, 2022. M. Xia, M. Artetxe, J. Du, D. Chen, and V. Stoyanov. paper code
Continual training of language models for few-shot learning, in EMNLP, 2022. Z. Ke, H. Lin, Y. Shao, H. Xu, L. Shu, and B. Liu. paper code
GPS: Genetic prompt search for efficient few-shot learning, in EMNLP, 2022. H. Xu, Y. Chen, Y. Du, N. Shao, Y. Wang, H. Li, and Z. Yang. paper code
On measuring the intrinsic few-shot hardness of datasets, in EMNLP, 2022. X. Zhao, S. Murty, and C. D. Manning. paper code
AMAL: Meta knowledge-driven few-shot adapter learning, in EMNLP, 2022. S. K. Hong, and T. Y. Jang. paper
Flamingo: A visual language model for few-shot learning, in NeurIPS, 2022. J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds, R. Ring, E. Rutherford, S. Cabi, T. Han, Z. Gong, S. Samangooei, M. Monteiro, J. Menick, S. Borgeaud, A. Brock, A. Nematzadeh, S. Sharifzadeh, M. Binkowski, R. Barreira, O. Vinyals, A. Zisserman, and K. Simonyan. paper
Language models with image descriptors are strong few-shot video-language learners, in NeurIPS, 2022. Z. Wang, M. Li, R. Xu, L. Zhou, J. Lei, X. Lin, S. Wang, Z. Yang, C. Zhu, D. Hoiem, S.-F. Chang, M. Bansal, and H. Ji. paper code
Singular value fine-tuning: Few-shot segmentation requires few-parameters fine-tuning, in NeurIPS, 2022. Y. Sun, Q. Chen, X. He, J. Wang, H. Feng, J. Han, E. Ding, J. Cheng, Z. Li, and J. Wang. paper code
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning, in NeurIPS, 2022. H. Liu, D. Tam, M. Mohammed, J. Mohta, T. Huang, M. Bansal, and C. Raffel. paper code
Powering finetuning in few-shot learning: Domain-agnostic bias reduction with selected sampling, in AAAI, 2022. R. Tao, H. Zhang, Y. Zheng, and M. Savvides. paper
SELECTION-INFERENCE: Exploiting large language models for interpretable logical reasoning, in ICLR, 2023. A. Creswell, M. Shannahan, and I. Higgins. paper
Revisit finetuning strategy for few-shot learning to transfer the emdeddings, in ICLR, 2023. H. Wang, T. Yue, X. Ye, Z. He, B. Li, and Y. Li. paper [code](https://github.com/whzyf951620/ LinearProbingFinetuningFirthBias)
Model ensemble instead of prompt fusion: A sample-specific knowledge transfer method for few-shot prompt tuning, in ICLR, 2023. X. PENG, C. Xing, P. K. Choubey, C.-S. Wu, and C. Xiong. paper
Bidirectional language models are also few-shot learners, in ICLR, 2023. A. Patel, B. Li, M. S. Rasooli, N. Constant, C. Raffel, and C. Callison-Burch. paper
Prototypical calibration for few-shot learning of language models, in ICLR, 2023. Z. Han, Y. Hao, L. Dong, Y. Sun, and F. Wei. paper code
Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners, in CVPR, 2023. R. Zhang, X. Hu, B. Li, S. Huang, H. Deng, Y. Qiao, P. Gao, and H. Li. paper code
Supervised masked knowledge distillation for few-shot transformers, in CVPR, 2023. H. Lin, G. Han, J. Ma, S. Huang, X. Lin, and S.-F. Chang. paper code
Boosting transductive few-shot fine-tuning with margin-based uncertainty weighting and probability regularization, in CVPR, 2023. R. Tao, H. Chen, and M. Savvides. paper
Hint-Aug: Drawing hints from foundation vision transformers towards boosted few-shot parameter-efficient tuning, in CVPR, 2023. Z. Yu, S. Wu, Y. Fu, S. Zhang, and Y. C. Lin. paper code
ProD: Prompting-to-disentangle domain knowledge for cross-domain few-shot image classification, in CVPR, 2023. T. Ma, Y. Sun, Z. Yang, and Y. Yang. paper
Few-shot learning with visual distribution calibration and cross-modal distribution alignment, in CVPR, 2023. R. Wang, H. Zheng, X. Duan, J. Liu, Y. Lu, T. Wang, S. Xu, and B. Zhang. paper code
MetricPrompt: Prompting model as a relevance metric for few-shot text classification, in KDD, 2023. H. Dong, W. Zhang, and W. Che. paper code
Efficient training of language models using few-shot learning, in ICML, 2023. S. J. Reddi, S. Miryoosefi, S. Karp, S. Krishnan, S. Kale, S. Kim, and S. Kumar. paper
Multitask pre-training of modular prompt for chinese few-shot learning, in ACL, 2023. T. Sun, Z. He, Q. Zhu, X. Qiu, and X. Huang. paper code
Cold-start data selection for better few-shot language model fine-tuning: A prompt-based uncertainty propagation approach, in ACL, 2023. Y. Yu, R. Zhang, R. Xu, J. Zhang, J. Shen, and C. Zhang. paper code
Instruction induction: From few examples to natural language task descriptions, in ACL, 2023. O. Honovich, U. Shaham, S. R. Bowman, and O. Levy. paper code
Few-shot adaptation works with unpredictable data, in ACL, 2023. J. S. Chan, M. Pieler, J. Jao, J. Scheurer, and E. Perez. paper
Hierarchical verbalizer for few-shot hierarchical text classification, in ACL, 2023. K. Ji, Y. Lian, J. Gao, and B. Wang. paper code
Black box few-shot adaptation for vision-language models, in ICCV, 2023. Y. Ouali, A. Bulat, B. Matinez, and G. Tzimiropoulos. paper code
Read-only prompt optimization for vision-language few-shot learning, in ICCV, 2023. D. Lee, S. Song, J. Suh, J. Choi, S. Lee, and H. J. Kim. paper code
Not all features matter: Enhancing few-shot CLIP with adaptive prior refinement, in ICCV, 2023. X. Zhu, R. Zhang, B. He, A. Zhou, D. Wang, B. Zhao, and P. Gao. paper code
One-shot generative domain adaptation, in ICCV, 2023. C. Yang, Y. Shen, Z. Zhang, Y. Xu, J. Zhu, Z. Wu, and B. Zhou. paper code
Smoothness similarity regularization for few-shot GAN adaptation, in ICCV, 2023. V. Sushko, R. Wang, and J. Gall. paper
Task-aware adaptive learning for cross-domain few-shot learning, in ICCV, 2023. Y. Guo, R. Du, Y. Dong, T. Hospedales, Y. Song, and Z. Ma. paper code
Defending pre-trained language models as few-shot learners against backdoor attacks, in NeurIPS, 2023. Z. Xi, T. Du, C. Li, R. Pang, S. Ji, J. Chen, F. Ma, and T. Wang. paper code
FD-Align: Feature discrimination alignment for fine-tuning pre-trained models in few-shot learning, in NeurIPS, 2023. K. Song, H. Ma, B. Zou, H. Zhang, and W. Huang. paper code
Fairness-guided few-shot prompting for large language models, in NeurIPS, 2023. H. Ma, C. Zhang, Y. Bian, L. Liu, Z. Zhang, P. Zhao, S. Zhang, H. Fu, Q. Hu, and B. Wu. paper
Meta-Adapter: An online few-shot learner for vision-language model, in NeurIPS, 2023. C. Cheng, L. Song, R. Xue, H. Wang, H. Sun, Y. Ge, and Y. Shan. paper
Language models can improve event prediction by few-shot abductive reasoning, in NeurIPS, 2023. X. Shi, S. Xue, K. Wang, F. Zhou, J. Y. Zhang, J. ZHOU, C. Tan, and H. Mei. paper code
ExPT: Synthetic pretraining for few-shot experimental design, in NeurIPS, 2023. T. Nguyen, S. Agrawal, and A. Grover. paper code
LoCoOp: Few-shot out-of-distribution detection via prompt learning, in NeurIPS, 2023. A. Miyai, Q. Yu, G. Irie, and K. Aizawa. paper code
Embroid: Unsupervised prediction smoothing can improve few-shot classification, in NeurIPS, 2023. N. Guha, M. F. Chen, K. Bhatia, A. Mirhoseini, F. Sala, and C. Re. paper
Domain re-modulation for few-shot generative domain adaptation, in NeurIPS, 2023. Y. Wu, Z. Li, C. Wang, H. Zheng, S. Zhao, B. Li, and D. Tao. paper code
Focus your attention when few-shot classification, in NeurIPS, 2023. H. Wang, S. Jie, and Z. Deng. paper code
The effect of diversity in meta-learning, in AAAI, 2023. R. Kumar, T. Deleu, and Y. Bengio. paper code
FEditNet: Few-shot editing of latent semantics in GAN spaces, in AAAI, 2023. M. Xia, Y. Shu, Y. Wang, Y.-K. Lai, Q. Li, P. Wan, Z. Wang, and Y.-J. Liu. paper code
Better generalized few-shot learning even without base data., in AAAI, 2023. S.-W. Kim, and D.-W. Choi. paper code
Prompt-augmented linear probing: Scaling beyond the limit of few-shot in-context learners, in AAAI, 2023. H. Cho, H. J. Kim, J. Kim, S.-W. Lee, S.-g. Lee, K. M. Yoo, and T. Kim. paper
Anchoring fine-tuning of sentence transformer with semantic label information for efficient truly few-shot classification, in EMNLP, 2023. A. Pauli, L. Derczynski, and I. Assent. paper code
Skill-based few-shot selection for in-context learning, in EMNLP, 2023. S. An, B. Zhou, Z. Lin, Q. Fu, B. Chen, N. Zheng, W. Chen, and J.-G. Lou. paper
Transductive learning for textual few-shot classification in API-based embedding models, in EMNLP, 2023. P. Colombo, V. Pellegrain, M. Boudiaf, M. Tami, V. Storchan, I. Ayed, and P. Piantanida. paper
AdaSent: Efficient domain-adapted sentence embeddings for few-shot classification, in EMNLP, 2023. Y. Huang, K. Wang, S. Dutta, R. Patel, G. Glavaš, and I. Gurevych. paper code
A hard-to-beat baseline for training-free CLIP-based adaptation, in ICLR, 2024. Z. Wang, J. Liang, L. Sheng, R. He, Z. Wang, and T. Tan. paper
Group preference optimization: Few-shot alignment of large language models, in ICLR, 2024. S. Zhao, J. Dang, and A. Grover. paper
Consistency-guided prompt learning for vision-language models, in ICLR, 2024. S. Roy, and A. Etemad. paper code
BayesPrompt: Prompting large-scale pre-trained language models on few-shot inference via debiased domain abstraction, in ICLR, 2024. J. Li, F. Song, Y. Jin, W. Qiang, C. Zheng, F. Sun, and H. Xiong. paper
Neural fine-tuning search for few-shot learning, in ICLR, 2024. P. Eustratiadis, Ł. Dudziak, D. Li, and T. Hospedales. paper
DePT: Decomposed prompt tuning for parameter-efficient fine-tuning, in ICLR, 2024. Z. Shi, and A. Lipani. paper code
Few-shot hybrid domain adaptation of image generator, in ICLR, 2024. H. Li, Y. Liu, L. Xia, Y. Lin, T. Zheng, Z. Yang, W. Wang, X. Zhong, X. Ren, and X. He. paper

Refining Meta-learned Parameters

Model-agnostic meta-learning for fast adaptation of deep networks, in ICML, 2017. C. Finn, P. Abbeel, and S. Levine. paper
Bayesian model-agnostic meta-learning, in NeurIPS, 2018. J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn. paper
Probabilistic model-agnostic meta-learning, in NeurIPS, 2018. C. Finn, K. Xu, and S. Levine. paper
Gradient-based meta-learning with learned layerwise metric and subspace, in ICML, 2018. Y. Lee and S. Choi. paper
Recasting gradient-based meta-learning as hierarchical Bayes, in ICLR, 2018. E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths. paper
Few-shot human motion prediction via meta-learning, in ECCV, 2018. L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura. paper
The effects of negative adaptation in model-agnostic meta-learning, arXiv preprint, 2018. T. Deleu and Y. Bengio. paper
Unsupervised meta-learning for few-shot image classification, in NeurIPS, 2019. S. Khodadadeh, L. Bölöni, and M. Shah. paper
Amortized bayesian meta-learning, in ICLR, 2019. S. Ravi and A. Beatson. paper
Meta-learning with latent embedding optimization, in ICLR, 2019. A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell. paper code
LGM-Net: Learning to generate matching networks for few-shot learning, in ICML, 2019. H. Li, W. Dong, X. Mei, C. Ma, F. Huang, and B.-G. Hu. paper code
Meta R-CNN: Towards general solver for instance-level low-shot learning, in ICCV, 2019. X. Yan, Z. Chen, A. Xu, X. Wang, X. Liang, and L. Lin. paper
Task agnostic meta-learning for few-shot learning, in CVPR, 2019. M. A. Jamal, and G.-J. Qi. paper
Meta-transfer learning for few-shot learning, in CVPR, 2019. Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele. paper code
Meta-learning of neural architectures for few-shot learning, in CVPR, 2020. T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter. paper
Attentive weights generation for few shot learning via information maximization, in CVPR, 2020. Y. Guo, and N.-M. Cheung. paper
Few-shot open-set recognition using meta-learning, in CVPR, 2020. B. Liu, H. Kang, H. Li, G. Hua, and N. Vasconcelos. paper
Incremental few-shot object detection, in CVPR, 2020. J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, and T. Xiang. paper
Automated relational meta-learning, in ICLR, 2020. H. Yao, X. Wu, Z. Tao, Y. Li, B. Ding, R. Li, and Z. Li. paper
Meta-learning with warped gradient descent, in ICLR, 2020. S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell. paper
Meta-learning without memorization, in ICLR, 2020. M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn. paper
ES-MAML: Simple Hessian-free meta learning, in ICLR, 2020. X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang. paper
Self-supervised tuning for few-shot segmentation, in IJCAI, 2020. K. Zhu, W. Zhai, and Y. Cao. paper
Multi-attention meta learning for few-shot fine-grained image recognition, in IJCAI, 2020. Y. Zhu, C. Liu, and S. Jiang. paper
An ensemble of epoch-wise empirical Bayes for few-shot learning, in ECCV, 2020. Y. Liu, B. Schiele, and Q. Sun. paper code
Incremental few-shot meta-learning via indirect discriminant alignment, in ECCV, 2020. Q. Liu, O. Majumder, A. Achille, A. Ravichandran, R. Bhotika, and S. Soatto. paper
Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning, in ECCV, 2020. J. Kim, H. Kim, and G. Kim. paper code
Bayesian meta-learning for the few-shot setting via deep kernels, in NeurIPS, 2020. M. Patacchiola, J. Turner, E. J. Crowley, M. O'Boyle, and A. J. Storkey. paper code
OOD-MAML: Meta-learning for few-shot out-of-distribution detection and classification, in NeurIPS, 2020. T. Jeong, and H. Kim. paper code
Unraveling meta-learning: Understanding feature representations for few-shot tasks, in ICML, 2020. M. Goldblum, S. Reich, L. Fowl, R. Ni, V. Cherepanova, and T. Goldstein. paper code
Node classification on graphs with few-shot novel labels via meta transformed network embedding, in NeurIPS, 2020. L. Lan, P. Wang, X. Du, K. Song, J. Tao, and X. Guan. paper
Adversarially robust few-shot learning: A meta-learning approach, in NeurIPS, 2020. M. Goldblum, L. Fowl, and T. Goldstein. paper code
BOIL: Towards representation change for few-shot learning, in ICLR, 2021. J. Oh, H. Yoo, C. Kim, and S. Yun. paper code
Few-shot open-set recognition by transformation consistency, in CVPR, 2021. M. Jeong, S. Choi, and C. Kim. paper
Improving generalization in meta-learning via task augmentation, in ICML, 2021. H. Yao, L. Huang, L. Zhang, Y. Wei, L. Tian, J. Zou, J. Huang, and Z. Li. paper
A representation learning perspective on the importance of train-validation splitting in meta-learning, in ICML, 2021. N. Saunshi, A. Gupta, and W. Hu. paper code
Data augmentation for meta-learning, in ICML, 2021. R. Ni, M. Goldblum, A. Sharaf, K. Kong, and T. Goldstein. paper code
Task cooperation for semi-supervised few-shot learning, in AAAI, 2021. H. Ye, X. Li, and D.-C. Zhan. paper
Conditional self-supervised learning for few-shot classification, in IJCAI, 2021. Y. An, H. Xue, X. Zhao, and L. Zhang. paper
Cross-domain few-shot classification via adversarial task augmentation, in IJCAI, 2021. H. Wang, and Z.-H. Deng. paper code
DReCa: A general task augmentation strategy for few-shot natural language inference, in NAACL-HLT, 2021. S. Murty, T. Hashimoto, and C. D. Manning. paper
MetaXL: Meta representation transformation for low-resource cross-lingual learning, in NAACL-HLT, 2021. M. Xia, G. Zheng, S. Mukherjee, M. Shokouhi, G. Neubig, and A. H. Awadallah. paper code
Meta-learning with task-adaptive loss function for few-shot learning, in ICCV, 2021. S. Baik, J. Choi, H. Kim, D. Cho, J. Min, and K. M. Lee. paper code
Meta-Baseline: Exploring simple meta-learning for few-shot learning, in ICCV, 2021. Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang. paper
A lazy approach to long-horizon gradient-based meta-learning, in ICCV, 2021. M. A. Jamal, L. Wang, and B. Gong. paper
Task-aware part mining network for few-shot learning, in ICCV, 2021. J. Wu, T. Zhang, Y. Zhang, and F. Wu. paper
Binocular mutual learning for improving few-shot classification, in ICCV, 2021. Z. Zhou, X. Qiu, J. Xie, J. Wu, and C. Zhang. paper code
Meta-learning with an adaptive task scheduler, in NeurIPS, 2021. H. Yao, Y. Wang, Y. Wei, P. Zhao, M. Mahdavi, D. Lian, and C. Finn. paper
Memory efficient meta-learning with large images, in NeurIPS, 2021. J. Bronskill, D. Massiceti, M. Patacchiola, K. Hofmann, S. Nowozin, and R. Turner. paper
EvoGrad: Efficient gradient-based meta-learning and hyperparameter optimization, in NeurIPS, 2021. O. Bohdal, Y. Yang, and T. Hospedales. paper
Towards enabling meta-learning from target models, in NeurIPS, 2021. S. Lu, H. Ye, L. Gan, and D. Zhan. paper
The role of global labels in few-shot classification and how to infer them, in NeurIPS, 2021. R. Wang, M. Pontil, and C. Ciliberto. paper
How to train your MAML to excel in few-shot classification, in ICLR, 2022. H. Ye, and W. Chao. paper code
Meta-learning with fewer tasks through task interpolation, in ICLR, 2022. H. Yao, L. Zhang, and C. Finn. paper code
Continuous-time meta-learning with forward mode differentiation, in ICLR, 2022. T. Deleu, D. Kanaa, L. Feng, G. Kerg, Y. Bengio, G. Lajoie, and P. Bacon. paper
Bootstrapped meta-learning, in ICLR, 2022. S. Flennerhag, Y. Schroecker, T. Zahavy, H. v. Hasselt, D. Silver, and S. Singh. paper
Learning prototype-oriented set representations for meta-learning, in ICLR, 2022. D. d. Guo, L. Tian, M. Zhang, M. Zhou, and H. Zha. paper
Dynamic kernel selection for improved generalization and memory efficiency in meta-learning, in CVPR, 2022. A. Chavan, R. Tiwari, U. Bamba, and D. K. Gupta. paper code
What matters for meta-learning vision regression tasks?, in CVPR, 2022. N. Gao, H. Ziesche, N. A. Vien, M. Volpp, and G. Neumann. paper code
Multidimensional belief quantification for label-efficient meta-learning, in CVPR, 2022. D. S. Pandey, and Q. Yu. paper
Few-shot node classification on attributed networks with graph meta-learning, in SIGIR, 2022. Y. Liu, M. Li, X. Li, F. Giunchiglia, X. Feng, and R. Guan. paper
The role of deconfounding in meta-learning, in ICML, 2022. Y. Jiang, Z. Chen, K. Kuang, L. Yuan, X. Ye, Z. Wang, F. Wu, and Y. Wei. paper
Stochastic deep networks with linear competing units for model-agnostic meta-learning, in ICML, 2022. K. Kalais, and S. Chatzis. paper code
Efficient variance reduction for meta-learning, in ICML, 2022. H. Yang, and J. T. Kwok. paper
Subspace learning for effective meta-learning, in ICML, 2022. W. Jiang, J. Kwok, and Y. Zhang. paper
Robust meta-learning with sampling noise and label noise via Eigen-Reptile, in ICML, 2022. D. Chen, L. Wu, S. Tang, X. Yun, B. Long, and Y. Zhuang. paper code
Attentional meta-learners for few-shot polythetic classification, in ICML, 2022. B. J. Day, R. V. Torné, N. Simidjievski, and P. Lió. paper code
PLATINUM: Semi-supervised model agnostic meta-learning using submodular mutual information, in ICML, 2022. C. Li, S. Kothawade, F. Chen, and R. K. Iyer. paper code
Finding meta winning ticket to train your MAML, in KDD, 2022. D. Gao, Y. Xie, Z. Zhou, Z. Wang, Y. Li, and B. Ding. paper
p-Meta: Towards on-device deep model adaptation, in KDD, 2022. Z. Qu, Z. Zhou, Y. Tong, and L. Thiele. paper
FAITH: Few-shot graph classification with hierarchical task graphs, in IJCAI, 2022. S. Wang, Y. Dong, X. Huang, C. Chen, and J. Li. paper code
Meta-learning fast weight language models, in EMNLP, 2022. K. Clark, K. Guu, M.-W. Chang, P. Pasupat, G. Hinton, and M. Norouzi. paper
Understanding benign overfitting in gradient-based meta learning, in NeurIPS, 2022. L. Chen, S. Lu, and T. Chen. paper
Meta-learning with self-improving momentum target, in NeurIPS, 2022. J. Tack, J. Park, H. Lee, J. Lee, and J. Shin. paper
Adversarial task up-sampling for meta-learning, in NeurIPS, 2022. Y. Wu, L.-K. Huang, and Y. Wei. paper
PAC prediction sets for meta-learning, in NeurIPS, 2022. S. Park, E. Dobriban, I. Lee, and O. Bastani. paper
A contrastive rule for meta-learning, in NeurIPS, 2022. N. Zucchet, S. Schug, J. V. Oswald, D. Zhao, and J. Sacramento. paper code
On enforcing better conditioned meta-learning for rapid few-shot adaptation, in NeurIPS, 2022. M. Hiller, M. Harandi, and T. Drummond. paper
Conditional meta-learning of linear representations, in NeurIPS, 2022. G. Denevi, m. pontil, and C. Ciliberto. paper
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks, in NeurIPS, 2022. D. Chijiwa, S. Yamaguchi, A. Kumagai, and Y. Ida. paper code
MetaNODE: Prototype optimization as a neural ODE for few-shot learning, in AAAI, 2022. B. Zhang, X. Li, S. Feng, Y. Ye, and R. Ye. paper
A nested bi-level optimization framework for robust few shot learning, in AAAI, 2022. K. Killamsetty, C. Li, C. Zhao, F. Chen, and R. K. Iyer. paper
Enhancing meta learning via multi-objective soft improvement functions, in ICLR, 2023. R. Yu, W. Chen, X. Wang, and J. Kwok. paper
Understanding train-validation split in meta-learning with neural networks, in ICLR, 2023. X. Zuo, Z. Chen, H. Yao, Y. Cao, and Q. Gu. paper
Bi-level meta-learning for few-shot domain generalization, in CVPR, 2023. X. Qin, X. Song, and S. Jiang. paper
SHOT: Suppressing the hessian along the optimization trajectory for gradient-based meta-learning, in NeurIPS, 2023. J. Lee, J. Yoo, and N. Kwak. paper code
Meta-AdaM: An meta-learned adaptive optimizer with momentum for few-shot learning, in NeurIPS, 2023. S. Sun, and H. Gao. paper
ESPT: A self-supervised episodic spatial pretext task for improving few-shot learning, in AAAI, 2023. Y. Rong, X. Lu, Z. Sun, Y. Chen, and S. Xiong. paper code
Scalable bayesian meta-learning through generalized implicit gradients, in AAAI, 2023. Y. Zhang, B. Li, S. Gao, and G. B. Giannakis. paper code
A hierarchical Bayesian model for few-shot meta learning, in ICLR, 2024. M. Kim, and T. Hospedales. paper
First-order ANIL provably learns representations despite overparametrisation, in ICLR, 2024. O. K. Yüksel, E. Boursier, and N. Flammarion. paper
Meta-learning priors using unrolled proximal neural networks, in ICLR, 2024. Y. Zhang, and G. B. Giannakis. paper

Learning Search Steps

Optimization as a model for few-shot learning, in ICLR, 2017. S. Ravi and H. Larochelle. paper code
Meta Navigator: Search for a good adaptation policy for few-shot learning, in ICCV, 2021. C. Zhang, H. Ding, G. Lin, R. Li, C. Wang, and C. Shen. paper

Applications

Computer Vision

Learning robust visual-semantic embeddings, in CVPR, 2017. Y.-H. Tsai, L.-K. Huang, and R. Salakhutdinov. paper
One-shot action localization by learning sequence matching network, in CVPR, 2018. H. Yang, X. He, and F. Porikli. paper
Incremental few-shot learning for pedestrian attribute recognition, in EMNLP, 2018. L. Xiang, X. Jin, G. Ding, J. Han, and L. Li. paper
Few-shot video-to-video synthesis, in NeurIPS, 2019. T.-C. Wang, M.-Y. Liu, A. Tao, G. Liu, J. Kautz, and B. Catanzaro. paper code
Few-shot object detection via feature reweighting, in ICCV, 2019. B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell. paper code
Few-shot unsupervised image-to-image translation, in ICCV, 2019. M.-Y. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, and J. Kautz. paper code
Feature weighting and boosting for few-shot segmentation, in ICCV, 2019. K. Nguyen, and S. Todorovic. paper
Few-shot adaptive gaze estimation, in ICCV, 2019. S. Park, S. D. Mello, P. Molchanov, U. Iqbal, O. Hilliges, and J. Kautz. paper
AMP: Adaptive masked proxies for few-shot segmentation, in ICCV, 2019. M. Siam, B. N. Oreshkin, and M. Jagersand. paper code
Few-shot generalization for single-image 3D reconstruction via priors, in ICCV, 2019. B. Wallace, and B. Hariharan. paper
Few-shot adversarial learning of realistic neural talking head models, in ICCV, 2019. E. Zakharov, A. Shysheya, E. Burkov, and V. Lempitsky. paper code
Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation, in ICCV, 2019. C. Zhang, G. Lin, F. Liu, J. Guo, Q. Wu, and R. Yao. paper
Time-conditioned action anticipation in one shot, in CVPR, 2019. Q. Ke, M. Fritz, and B. Schiele. paper
Few-shot learning with localization in realistic settings, in CVPR, 2019. D. Wertheimer, and B. Hariharan. paper code
Improving few-shot user-specific gaze adaptation via gaze redirection synthesis, in CVPR, 2019. Y. Yu, G. Liu, and J.-M. Odobez. paper
CANet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning, in CVPR, 2019. C. Zhang, G. Lin, F. Liu, R. Yao, and C. Shen. paper code
Multi-level Semantic Feature Augmentation for One-shot Learning, in TIP, 2019. Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal. paper code
3FabRec: Fast few-shot face alignment by reconstruction, in CVPR, 2020. B. Browatzki, and C. Wallraven. paper
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Learning dynamic alignment via meta-filter for few-shot learning, in CVPR, 2021. C. Xu, Y. Fu, C. Liu, C. Wang, J. Li, F. Huang, L. Zhang, and X. Xue. paper
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Few-shot image generation via cross-domain correspondence, in CVPR, 2021. U. Ojha, Y. Li, J. Lu, A. A. Efros, Y. J. Lee, E. Shechtman, and R. Zhang. paper
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Anti-aliasing semantic reconstruction for few-shot semantic segmentation, in CVPR, 2021. B. Liu, Y. Ding, J. Jiao, X. Ji, and Q. Ye. paper
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Incremental few-shot instance segmentation, in CVPR, 2021. D. A. Ganea, B. Boom, and R. Poppe. paper code
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Few-shot font generation with localized style representations and factorization, in AAAI, 2021. S. Park, S. Chun, J. Cha, B. Lee, and H. Shim. paper code
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One-shot face reenactment using appearance adaptive normalization, in AAAI, 2021. G. Yao, Y. Yuan, T. Shao, S. Li, S. Liu, Y. Liu, M. Wang, and K. Zhou. paper
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Attribute group editing for reliable few-shot image generation, in CVPR, 2022. G. Ding, X. Han, S. Wang, S. Wu, X. Jin, D. Tu, and Q. Huang. paper code
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Dynamic prototype convolution network for few-shot semantic segmentation, in CVPR, 2022. J. Liu, Y. Bao, G. Xie, H. Xiong, J. Sonke, and E. Gavves. paper
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Attribute surrogates learning and spectral tokens pooling in transformers for few-shot learning, in CVPR, 2022. Y. He, W. Liang, D. Zhao, H. Zhou, W. Ge, Y. Yu, and W. Zhang. paper code
Task discrepancy maximization for fine-grained few-shot classification, in CVPR, 2022. S. Lee, W. Moon, and J. Heo. paper
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Iterative few-shot semantic segmentation from image label text, in IJCAI, 2022. H. Wang, L. Liu, W. Zhang, J. Zhang, Z. Gan, Y. Wang, C. Wang, and H. Wang. paper code
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Learning dense object descriptors from multiple views for low-shot category generalization, in NeurIPS, 2022. S. Stojanov, N. A. Thai, Z. Huang, and J. M. Rehg. paper code
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Few-shot geometry-aware keypoint localization, in CVPR, 2023. X. He, G. Bharaj, D. Ferman, H. Rhodin, and P. Garrido. paper code
AsyFOD: An asymmetric adaptation paradigm for few-shot domain adaptive object detection, in CVPR, 2023. Y. Gao, K.-Y. Lin, J. Yan, Y. Wang, and W.-S. Zheng. paper code
NIFF: Alleviating forgetting in generalized few-shot object detection via neural instance feature forging, in CVPR, 2023. K. Guirguis, J. Meier, G. Eskandar, M. Kayser, B. Yang, and J. Beyerer. paper
A strong baseline for generalized few-shot semantic segmentation, in CVPR, 2023. S. Hajimiri, M. Boudiaf, I. B. Ayed, and J. Dolz. paper code
StyleAdv: Meta style adversarial training for cross-domain few-shot learning, in CVPR, 2023. Y. Fu, Y. Xie, Y. Fu, and Y.-G. Jiang. paper code
BlendFields: Few-shot example-driven facial modeling, in CVPR, 2023. K. Kania, S. J. Garbin, A. Tagliasacchi, V. Estellers, K. M. Yi, J. Valentin, T. Trzcinski, and M. Kowalski. paper
Learning orthogonal prototypes for generalized few-shot semantic segmentation, in CVPR, 2023. S. Liu, Y. Zhang, Z. Qiu, H. Xie, Y. Zhang, and T. Yao. paper
DiGeo: Discriminative geometry-aware learning for generalized few-shot object detection, in CVPR, 2023. J. Ma, Y. Niu, J. Xu, S. Huang, G. Han, and S.-F. Chang. paper code
Hierarchical dense correlation distillation for few-shot segmentation, in CVPR, 2023. B. Peng, Z. Tian, X. Wu, C. Wang, S. Liu, J. Su, and J. Jia. paper code
Rethinking the correlation in few-shot segmentation: A buoys view, in CVPR, 2023. Y. Wang, R. Sun, and T. Zhang. paper
CF-Font: Content fusion for few-shot font generation, in CVPR, 2023. C. Wang, M. Zhou, T. Ge, Y. Jiang, H. Bao, and W. Xu. paper code
MoLo: Motion-augmented long-short contrastive learning for few-shot action recognition, in CVPR, 2023. X. Wang, S. Zhang, Z. Qing, C. Gao, Y. Zhang, D. Zhao, and N. Sang. paper code
Active exploration of multimodal complementarity for few-shot action recognition, in CVPR, 2023. Y. Wanyan, X. Yang, C. Chen, and C. Xu. paper
Generating features with increased crop-related diversity for few-shot object detection, in CVPR, 2023. J. Xu, H. Le, and D. Samaras. paper
SMAE: Few-shot learning for HDR deghosting with saturation-aware masked autoencoders, in CVPR, 2023. Q. Yan, S. Zhang, W. Chen, H. Tang, Y. Zhu, J. Sun, L. V. Gool, and Y. Zhang. paper
MIANet: Aggregating unbiased instance and general information for few-shot semantic segmentation, in CVPR, 2023. Y. Yang, Q. Chen, Y. Feng, and T. Huang. paper code
FreeNeRF: Improving few-shot neural rendering with free frequency regularization, in CVPR, 2023. J. Yang, M. Pavone, and Y. Wang. paper code
Exploring incompatible knowledge transfer in few-shot image generation, in CVPR, 2023. Y. Zhao, C. Du, M. Abdollahzadeh, T. Pang, M. Lin, S. Yan, and N.-M. Cheung. paper code
Where is my spot? few-shot image generation via latent subspace optimization, in CVPR, 2023. C. Zheng, B. Liu, H. Zhang, X. Xu, and S. He. paper code
Distilling self-supervised vision transformers for weakly-supervised few-shot classification & segmentation, in CVPR, 2023. D. Kang, P. Koniusz, M. Cho, and N. Murray. paper
FGNet: Towards filling the intra-class and inter-class gaps for few-shot segmentation, in IJCAI, 2023. Y. Zhang, W. Yang, and S. Wang. paper code
Clustered-patch element connection for few-shot learning, in IJCAI, 2023. J. Lai, S. Yang, J. Zhou, W. Wu, X. Chen, J. Liu, B.-B. Gao, and C. Wang. paper
GeCoNeRF: Few-shot neural radiance fields via geometric consistency, in ICML, 2023. M. Kwak, J. Song, and S. Kim. paper code
Few-shot common action localization via cross-attentional fusion of context and temporal dynamics, in ICCV, 2023. J. Lee, M. Jain, and S. Yun. paper
StyleDomain: Efficient and lightweight parameterizations of StyleGAN for one-shot and few-shot domain adaptation, in ICCV, 2023. A. Alanov, V. Titov, M. Nakhodnov, and D. Vetrov. paper code
FlipNeRF: Flipped reflection rays for few-shot novel view synthesis, in ICCV, 2023. S. Seo, Y. Chang, and N. Kwak. paper
Few-shot physically-aware articulated mesh generation via hierarchical deformation, in ICCV, 2023. X. Liu, B. Wang, H. Wang, and L. Yi. paper code
SparseNeRF: Distilling depth ranking for few-shot novel view synthesis, in ICCV, 2023. G. Wang, Z. Chen, C. C. Loy, and Z. Liu. paper code
Few-shot video classification via representation fusion and promotion learning, in ICCV, 2023. H. Xia, K. Li, M. R. Min, and Z. Ding. paper
Augmenting and aligning snippets for few-shot video domain adaptation, in ICCV, 2023. Y. Xu, J. Yang, Y. Zhou, Z. Chen, M. Wu, and X. Li. paper code
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering, in ICCV, 2023. M. S. Drehwald, S. Eppel, J. Li, H. Hao, and A. Aspuru-Guzik. paper code
FS-DETR: Few-shot detection transformer with prompting and without re-training, in ICCV, 2023. A. Bulat, R. Guerrero, B. Martinez, and G. Tzimiropoulos. paper
Confidence-based visual dispersal for few-shot unsupervised domain adaptation, in ICCV, 2023. Y. Xiong, H. Chen, Z. Lin, S. Zhao, and G. Ding. paper code
CDFSL-V: Cross-domain few-shot learning for videos, in ICCV, 2023. S. Samarasinghe, M. N. Rizve, N. Kardan, and M. Shah. paper code
Generalized few-shot point cloud segmentation via geometric words, in ICCV, 2023. Y. Xu, C. Hu, N. Zhao, and G. H. Lee. paper code
Invariant training 2D-3D joint hard samples for few-shot point cloud recognition, in ICCV, 2023. X. Yi, J. Deng, Q. Sun, X. Hua, J. Lim, and H. Zhang. paper code
CIRI: Curricular inactivation for residue-aware one-shot video inpainting, in ICCV, 2023. W. Zheng, C. Xu, X. Xu, W. Liu, and S. He. paper code
s-Adaptive decoupled prototype for few-shot object detection, in ICCV, 2023. J. Du, S. Zhang, Q. Chen, H. Le, Y. Sun, Y. Ni, J. Wang, B. He, and J. Wang. paper
Parallel attention interaction network for few-shot skeleton-based action recognition, in ICCV, 2023. X. Liu, S. Zhou, L. Wang, and G. Hua. paper
Robust one-shot face video re-enactment using hybrid latent spaces of StyleGAN2, in ICCV, 2023. T. Oorloff, and Y. Yacoob. paper code
Informative data mining for one-shot cross-domain semantic segmentation, in ICCV, 2023. Y. Wang, J. Liang, J. Xiao, S. Mei, Y. Yang, and Z. Zhang. paper code
The euclidean space is evil: Hyperbolic attribute editing for few-shot image generation, in ICCV, 2023. L. Li, Y. Zhang, and S. Wang. paper code
Few shot font generation via transferring similarity guided global style and quantization local style, in ICCV, 2023. W. Pan, A. Zhu, X. Zhou, B. K. Iwana, and S. Li. paper code
Boosting few-shot action recognition with graph-guided hybrid matching, in ICCV, 2023. J. Xing, M. Wang, Y. Ruan, B. Chen, Y. Guo, B. Mu, G. Dai, J. Wang, and Y. Liu. paper code
MSI: Maximize support-set information for few-shot segmentation, in ICCV, 2023. S. Moon, S. S. Sohn, H. Zhou, S. Yoon, V. Pavlovic, M. H. Khan, and M. Kapadia. paper code
FastRecon: Few-shot industrial anomaly detection via fast feature reconstruction, in ICCV, 2023. Z. Fang, X. Wang, H. Li, J. Liu, Q. Hu, and J. Xiao. paper code
Self-calibrated cross attention network for few-shot segmentation, in ICCV, 2023. Q. Xu, W. Zhao, G. Lin, and C. Long. paper code
Multi-grained temporal prototype learning for few-shot video object segmentation, in ICCV, 2023. N. Liu, K. Nan, W. Zhao, Y. Liu, X. Yao, S. Khan, H. Cholakkal, R. M. Anwer, J. Han, and F. S. Khan. paper code
HyperReenact: One-shot reenactment via jointly learning to refine and retarget faces, in ICCV, 2023. S. Bounareli, C. Tzelepis, V. Argyriou, I. Patras, and G. Tzimiropoulos. paper code
General image-to-image translation with one-shot image guidance, in ICCV, 2023. B. Cheng, Z. Liu, Y. Peng, and Y. Lin. paper code
ActorsNeRF: Animatable few-shot human rendering with generalizable NeRFs, in ICCV, 2023. J. Mu, S. Sang, N. Vasconcelos, and X. Wang. paper code
One-shot implicit animatable avatars with model-based priors, in ICCV, 2023. Y. Huang, H. Yi, W. Liu, H. Wang, B. Wu, W. Wang, B. Lin, D. Zhang, and D. Cai. paper code
Preface: A data-driven volumetric prior for few-shot ultra high-resolution face synthesis, in ICCV, 2023. M. C. Bühler, K. Sarkar, T. Shah, G. Li, D. Wang, L. Helminger, S. Orts-Escolano, D. Lagun, O. Hilliges, T. Beeler, and A. Meka. paper
DINAR: Diffusion inpainting of neural textures for one-shot human avatars, in ICCV, 2023. D. Svitov, D. Gudkov, R. Bashirov, and V. Lempitsky. paper
Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation, in ICCV, 2023. J. Z. Wu, Y. Ge, X. Wang, S. W. Lei, Y. Gu, Y. Shi, W. Hsu, Y. Shan, X. Qie, and M. Z. Shou. paper code
Phasic content fusing diffusion model with directional distribution consistency for few-shot model adaption, in ICCV, 2023. T. Hu, J. Zhang, L. Liu, R. Yi, S. Kou, H. Zhu, X. Chen, Y. Wang, C. Wang, and L. Ma. paper code
Prototypical variational autoencoder for 3D few-shot object detection, in NeurIPS, 2023. W. Tang, B. YANG, X. Li, Y. Liu, P. Heng, and C. Fu. paper
Generalizable one-shot 3D neural head avatar, in NeurIPS, 2023. X. Li, S. D. Mello, S. Liu, K. Nagano, U. Iqbal, and J. Kautz. paper code
Focus on query: Adversarial mining transformer for few-shot segmentation, in NeurIPS, 2023. Y. Wang, N. Luo, and T. Zhang. paper code
Bi-directional feature reconstruction network for fine-grained few-shot image classification, in AAAI, 2023. J. Wu, D. Chang, A. Sain, X. Li, Z. Ma, J. Cao, J. Guo, and Y.-Z. Song. paper code
Revisiting the spatial and temporal modeling for few-shot action recognition, in AAAI, 2023. J. Xing, M. Wang, Y. Liu, and B. Mu. paper
Disentangle and remerge: Interventional knowledge distillation for few-shot object detection from a conditional causal perspective, in AAAI, 2023. J. Li, Y. Zhang, W. Qiang, L. Si, C. Jiao, X. Hu, C. Zheng, and F. Sun. paper code
Breaking immutable: Information-coupled prototype elaboration for few-shot object detection, in AAAI, 2023. X. Lu, W. Diao, Y. Mao, J. Li, P. Wang, X. Sun, and K. Fu. paper code
Few-shot object detection via variational feature aggregation, in AAAI, 2023. J. Han, Y. Ren, J. Ding, K. Yan, and G.-S. Xia. paper
Few-shot 3D point cloud semantic segmentation via stratified class-specific attention based transformer network, in AAAI, 2023. C. Zhang, Z. Wu, X. Wu, Z. Zhao, and S. Wang. paper code
Few-shot composition learning for image retrieval with prompt tuning, in AAAI, 2023. J. Wu, R. Wang, H. Zhao, R. Zhang, C. Lu, S. Li, and R. Henao. paper
Real3D-Portrait: One-shot realistic 3D talking portrait synthesis, in ICLR, 2024. Z. Ye, T. Zhong, Y. Ren, J. Yang, W. Li, J. Huang, Z. Jiang, J. He, R. Huang, J. Liu, C. Zhang, X. Yin, Z. MA, and Z. Zhao. paper code
Personalize segment anything model with one shot, in ICLR, 2024. R. Zhang, Z. Jiang, Z. Guo, S. Yan, J. Pan, H. Dong, Y. Qiao, P. Gao, and H. Li. paper
Matcher: Segment anything with one shot using all-purpose feature matching, in ICLR, 2024. Y. Liu, M. Zhu, H. Li, H. Chen, X. Wang, and C. Shen. paper
SparseDFF: Sparse-view feature distillation for one-shot dexterous manipulation, in ICLR, 2024. Q. Wang, H. Zhang, C. Deng, Y. You, H. Dong, Y. Zhu, and L. Guibas. paper

Robotics

Towards one shot learning by imitation for humanoid robots, in ICRA, 2010. Y. Wu and Y. Demiris. paper
Learning manipulation actions from a few demonstrations, in ICRA, 2013. N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss. paper
Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach, in ICRA, 2016. M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto. paper
One-shot imitation learning, in NeurIPS, 2017. Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba. paper
Meta-learning language-guided policy learning, in ICLR, 2019. J. D. Co-Reyes, A. Gupta, S. Sanjeev, N. Altieri, J. DeNero, P. Abbeel, and S. Levine. paper
Meta reinforcement learning with autonomous inference of subtask dependencies, in ICLR, 2020. S. Sohn, H. Woo, J. Choi, and H. Lee. paper
Watch, try, learn: Meta-learning from demonstrations and rewards, in ICLR, 2020. A. Zhou, E. Jang, D. Kappler, A. Herzog, M. Khansari, P. Wohlhart, Y. Bai, M. Kalakrishnan, S. Levine, and C. Finn. paper
Few-shot Bayesian imitation learning with logical program policies, in AAAI, 2020. T. Silver, K. R. Allen, A. K. Lew, L. P. Kaelbling, and J. Tenenbaum. paper
One solution is not all you need: Few-shot extrapolation via structured MaxEnt RL, in NeurIPS, 2020. S. Kumar, A. Kumar, S. Levine, and C. Finn. paper
Bowtie networks: Generative modeling for joint few-shot recognition and novel-view synthesis, in ICLR, 2021. Z. Bao, Y. Wang, and M. Hebert. paper
Demonstration-conditioned reinforcement learning for few-shot imitation, in ICML, 2021. C. R. Dance, J. Perez, and T. Cachet. paper
Hierarchical few-shot imitation with skill transition models, in ICLR, 2022. K. Hakhamaneshi, R. Zhao, A. Zhan, P. Abbeel, and M. Laskin. paper
Prompting decision transformer for few-shot policy generalization, in ICML, 2022. M. Xu, Y. Shen, S. Zhang, Y. Lu, D. Zhao, J. B. Tenenbaum, and C. Gan. paper code
Stage conscious attention network (SCAN): A demonstration-conditioned policy for few-shot imitation, in AAAI, 2022. J.-F. Yeh, C.-M. Chung, H.-T. Su, Y.-T. Chen, and W. H. Hsu. paper
Online prototype alignment for few-shot policy transfer, in ICML, 2023. Q. Yi, R. Zhang, S. Peng, J. Guo, Y. Gao, K. Yuan, R. Chen, S. Lan, X. Hu, Z. Du, X. Zhang, Q. Guo, and Y. Chen. paper code
LLM-planner: Few-shot grounded planning for embodied agents with large language models, in ICCV, 2023. C. H. Song, J. Wu, C. Washington, B. M. Sadler, W. Chao, and Y. Su. paper code
Where2Explore: Few-shot affordance learning for unseen novel categories of articulated objects, in NeurIPS, 2023. C. Ning, R. Wu, H. Lu, K. Mo, and H. Dong. paper
Skill machines: Temporal logic skill composition in reinforcement learning, in ICLR, 2024. G. N. Tasse, D. Jarvis, S. James, and B. Rosman. paper

Natural Language Processing

High-risk learning: Acquiring new word vectors from tiny data, in EMNLP, 2017. A. Herbelot and M. Baroni. paper
Few-shot representation learning for out-of-vocabulary words, in ACL, 2019. Z. Hu, T. Chen, K.-W. Chang, and Y. Sun. paper
Learning to customize model structures for few-shot dialogue generation tasks, in ACL, 2020. Y. Song, Z. Liu, W. Bi, R. Yan, and M. Zhang. paper
Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network, in ACL, 2020. Y. Hou, W. Che, Y. Lai, Z. Zhou, Y. Liu, H. Liu, and T. Liu. paper
Meta-reinforced multi-domain state generator for dialogue systems, in ACL, 2020. Y. Huang, J. Feng, M. Hu, X. Wu, X. Du, and S. Ma. paper
Universal natural language processing with limited annotations: Try few-shot textual entailment as a start, in EMNLP, 2020. W. Yin, N. F. Rajani, D. Radev, R. Socher, and C. Xiong. paper code
Simple and effective few-shot named entity recognition with structured nearest neighbor learning, in EMNLP, 2020. Y. Yang, and A. Katiyar. paper code
Discriminative nearest neighbor few-shot intent detection by transferring natural language inference, in EMNLP, 2020. J. Zhang, K. Hashimoto, W. Liu, C. Wu, Y. Wan, P. Yu, R. Socher, and C. Xiong. paper code
Few-shot learning for opinion summarization, in EMNLP, 2020. A. Bražinskas, M. Lapata, and I. Titov. paper code
Few-shot complex knowledge base question answering via meta reinforcement learning, in EMNLP, 2020. Y. Hua, Y. Li, G. Haffari, G. Qi, and T. Wu. paper code
Self-supervised meta-learning for few-shot natural language classification tasks, in EMNLP, 2020. T. Bansal, R. Jha, T. Munkhdalai, and A. McCallum. paper code
Uncertainty-aware self-training for few-shot text classification, in NeurIPS, 2020. S. Mukherjee, and A. Awadallah. paper code
Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction, in NeurIPS, 2020:. J. Baek, D. B. Lee, and S. J. Hwang. paper code
MetaNER: Named entity recognition with meta-learning, in WWW, 2020. J. Li, S. Shang, and L. Shao. paper
Conditionally adaptive multi-task learning: Improving transfer learning in NLP using fewer parameters & less data, in ICLR, 2021. J. Pilault, A. E. hattami, and C. Pal. paper code
Revisiting few-sample BERT fine-tuning, in ICLR, 2021. T. Zhang, F. Wu, A. Katiyar, K. Q. Weinberger, and Y. Artzi. paper code
Few-shot conversational dense retrieval, in SIGIR, 2021. S. Yu, Z. Liu, C. Xiong, T. Feng, and Z. Liu. paper code
Few-shot language coordination by modeling theory of mind, in ICML, 2021. H. Zhu, G. Neubig, and Y. Bisk. paper code
KEML: A knowledge-enriched meta-learning framework for lexical relation classification, in AAAI, 2021. C. Wang, M. Qiu, J. Huang, and X. He. paper
Few-shot learning for multi-label intent detection, in AAAI, 2021. Y. Hou, Y. Lai, Y. Wu, W. Che, and T. Liu. paper code
SALNet: Semi-supervised few-shot text classification with attention-based lexicon construction, in AAAI, 2021. J.-H. Lee, S.-K. Ko, and Y.-S. Han. paper
Learning from my friends: Few-shot personalized conversation systems via social networks, in AAAI, 2021. Z. Tian, W. Bi, Z. Zhang, D. Lee, Y. Song, and N. L. Zhang. paper code
Relative and absolute location embedding for few-shot node classification on graph, in AAAI, 2021. Z. Liu, Y. Fang, C. Liu, and S. C.H. Hoi. paper
Few-shot question answering by pretraining span selection, in ACL-IJCNLP, 2021. O. Ram, Y. Kirstain, J. Berant, A. Globerson, and O. Levy. paper code
A closer look at few-shot crosslingual transfer: The choice of shots matters, in ACL-IJCNLP, 2021. M. Zhao, Y. Zhu, E. Shareghi, I. Vulic, R. Reichart, A. Korhonen, and H. Schütze. paper code
Learning from miscellaneous other-classwords for few-shot named entity recognition, in ACL-IJCNLP, 2021. M. Tong, S. Wang, B. Xu, Y. Cao, M. Liu, L. Hou, and J. Li. paper code
Distinct label representations for few-shot text classification, in ACL-IJCNLP, 2021. S. Ohashi, J. Takayama, T. Kajiwara, and Y. Arase. paper code
Entity concept-enhanced few-shot relation extraction, in ACL-IJCNLP, 2021. S. Yang, Y. Zhang, G. Niu, Q. Zhao, and S. Pu. paper code
On training instance selection for few-shot neural text generation, in ACL-IJCNLP, 2021. E. Chang, X. Shen, H.-S. Yeh, and V. Demberg. paper code
Unsupervised neural machine translation for low-resource domains via meta-learning, in ACL-IJCNLP, 2021. C. Park, Y. Tae, T. Kim, S. Yang, M. A. Khan, L. Park, and J. Choo. paper code
Meta-learning with variational semantic memory for word sense disambiguation, in ACL-IJCNLP, 2021. Y. Du, N. Holla, X. Zhen, C. Snoek, and E. Shutova. paper code
Multi-label few-shot learning for aspect category detection, in ACL-IJCNLP, 2021. M. Hu, S. Z. H. Guo, C. Xue, H. Gao, T. Gao, R. Cheng, and Z. Su. paper
TextSETTR: Few-shot text style extraction and tunable targeted restyling, in ACL-IJCNLP, 2021. P. Rileya, N. Constantb, M. Guob, G. Kumarc, D. Uthusb, and Z. Parekh. paper
Few-shot text ranking with meta adapted synthetic weak supervision, in ACL-IJCNLP, 2021. S. Sun, Y. Qian, Z. Liu, C. Xiong, K. Zhang, J. Bao, Z. Liu, and P. Bennett. paper code
PROTAUGMENT: Intent detection meta-learning through unsupervised diverse paraphrasing, in ACL-IJCNLP, 2021. T. Dopierre, C. Gravier, and W. Logerais. paper code
AUGNLG: Few-shot natural language generation using self-trained data augmentation, in ACL-IJCNLP, 2021. X. Xu, G. Wang, Y.-B. Kim, and S. Lee. paper code
Meta self-training for few-shot neural sequence labeling, in KDD, 2021. Y. Wang, S. Mukherjee, H. Chu, Y. Tu, M. Wu, J. Gao, and A. H. Awadallah. paper code
Knowledge-enhanced domain adaptation in few-shot relation classification, in KDD, 2021. J. Zhang, J. Zhu, Y. Yang, W. Shi, C. Zhang, and H. Wang. paper code
Few-shot text classification with triplet networks, data augmentation, and curriculum learning, in NAACL-HLT, 2021. J. Wei, C. Huang, S. Vosoughi, Y. Cheng, and S. Xu. paper code
Few-shot intent classification and slot filling with retrieved examples, in NAACL-HLT, 2021. D. Yu, L. He, Y. Zhang, X. Du, P. Pasupat, and Q. Li. paper
Non-parametric few-shot learning for word sense disambiguation, in NAACL-HLT, 2021. H. Chen, M. Xia, and D. Chen. paper code
Towards few-shot fact-checking via perplexity, in NAACL-HLT, 2021. N. Lee, Y. Bang, A. Madotto, and P. Fung. paper
ConVEx: Data-efficient and few-shot slot labeling, in NAACL-HLT, 2021. M. Henderson, and I. Vulic. paper
Few-shot text generation with natural language instructions, in EMNLP, 2021. T. Schick, and H. Schütze. paper
Towards realistic few-shot relation extraction, in EMNLP, 2021. S. Brody, S. Wu, and A. Benton. paper code
Few-shot emotion recognition in conversation with sequential prototypical networks, in EMNLP, 2021. G. Guibon, M. Labeau, H. Flamein, L. Lefeuvre, and C. Clavel. paper code
Learning prototype representations across few-shot tasks for event detection, in EMNLP, 2021. V. Lai, F. Dernoncourt, and T. H. Nguyen. paper
Exploring task difficulty for few-shot relation extraction, in EMNLP, 2021. J. Han, B. Cheng, and W. Lu. paper code
Honey or poison? Solving the trigger curse in few-shot event detection via causal intervention, in EMNLP, 2021. J. Chen, H. Lin, X. Han, and L. Sun. paper code
Nearest neighbour few-shot learning for cross-lingual classification, in EMNLP, 2021. M. S. Bari, B. Haider, and S. Mansour. paper
Knowledge-aware meta-learning for low-resource text classification, in EMNLP, 2021. H. Yao, Y. Wu, M. Al-Shedivat, and E. P. Xing. paper code
Few-shot named entity recognition: An empirical baseline study, in EMNLP, 2021. J. Huang, C. Li, K. Subudhi, D. Jose, S. Balakrishnan, W. Chen, B. Peng, J. Gao, and J. Han. paper
MetaTS: Meta teacher-student network for multilingual sequence labeling with minimal supervision, in EMNLP, 2021. Z. Li, D. Zhang, T. Cao, Y. Wei, Y. Song, and B. Yin. paper
Meta-LMTC: Meta-learning for large-scale multi-label text classification, in EMNLP, 2021. R. Wang, X. Su, S. Long, X. Dai, S. Huang, and J. Chen. paper
Ontology-enhanced prompt-tuning for few-shot learning, in WWW, 2022. H. Ye, N. Zhang, S. Deng, X. Chen, H. Chen, F. Xiong, X. Chen, and H. Chen. paper
EICO: Improving few-shot text classification via explicit and implicit consistency regularization, in Findings of ACL, 2022. L. Zhao, and C. Yao. paper
Dialogue summaries as dialogue states (DS2), template-guided summarization for few-shot dialogue state tracking, in Findings of ACL, 2022. J. Shin, H. Yu, H. Moon, A. Madotto, and J. Park. paper code
A few-shot semantic parser for wizard-of-oz dialogues with the precise thingtalk representation, in Findings of ACL, 2022. G. Campagna, S. J. Semnani, R. Kearns, L. J. K. Sato, S. Xu, and M. S. Lam. paper
Multi-stage prompting for knowledgeable dialogue generation, in Findings of ACL, 2022. Z. Liu, M. Patwary, R. Prenger, S. Prabhumoye, W. Ping, M. Shoeybi, and B. Catanzaro. paper code
Few-shot named entity recognition with self-describing networks, in ACL, 2022. J. Chen, Q. Liu, H. Lin, X. Han, and L. Sun. paper code
CLIP models are few-shot learners: Empirical studies on VQA and visual entailment, in ACL, 2022. H. Song, L. Dong, W. Zhang, T. Liu, and F. Wei. paper
CONTaiNER: Few-shot named entity recognition via contrastive learning, in ACL, 2022. S. S. S. Das, A. Katiyar, R. J. Passonneau, and R. Zhang. paper code
Few-shot controllable style transfer for low-resource multilingual settings, in ACL, 2022. K. Krishna, D. Nathani, X. Garcia, B. Samanta, and P. Talukdar. paper
Label semantic aware pre-training for few-shot text classification, in ACL, 2022. A. Mueller, J. Krone, S. Romeo, S. Mansour, E. Mansimov, Y. Zhang, and D. Roth. paper
Inverse is better! Fast and accurate prompt for few-shot slot tagging, in Findings of ACL, 2022. Y. Hou, C. Chen, X. Luo, B. Li, and W. Che. paper
Label semantics for few shot named entity recognition, in Findings of ACL, 2022. J. Ma, M. Ballesteros, S. Doss, R. Anubhai, S. Mallya, Y. Al-Onaizan, and D. Roth. paper
Hierarchical recurrent aggregative generation for few-shot NLG, in Findings of ACL, 2022. G. Zhou, G. Lampouras, and I. Iacobacci. paper
Towards few-shot entity recognition in document images: A label-aware sequence-to-sequence framework, in Findings of ACL, 2022. Z. Wang, and J. Shang. paper
A good prompt is worth millions of parameters: Low-resource prompt-based learning for vision-language models, in ACL, 2022. W. Jin, Y. Cheng, Y. Shen, W. Chen, and X. Ren. paper code
Generated knowledge prompting for commonsense reasoning, in ACL, 2022. J. Liu, A. Liu, X. Lu, S. Welleck, P. West, R. L. Bras, Y. Choi, and H. Hajishirzi. paper code
End-to-end modeling via information tree for one-shot natural language spatial video grounding, in ACL, 2022. M. Li, T. Wang, H. Zhang, S. Zhang, Z. Zhao, J. Miao, W. Zhang, W. Tan, J. Wang, P. Wang, S. Pu, and F. Wu. paper
Leveraging task transferability to meta-learning for clinical section classification with limited data, in ACL, 2022. Z. Chen, J. Kim, R. Bhakta, and M. Y. Sir. paper
Improving meta-learning for low-resource text classification and generation via memory imitation, in ACL, 2022. Y. Zhao, Z. Tian, H. Yao, Y. Zheng, D. Lee, Y. Song, J. Sun, and N. L. Zhang. paper
A simple yet effective relation information guided approach for few-shot relation extraction, in Findings of ACL, 2022. Y. Liu, J. Hu, X. Wan, and T. Chang. paper code
Decomposed meta-learning for few-shot named entity recognition, in Findings of ACL, 2022. T. Ma, H. Jiang, Q. Wu, T. Zhao, and C. Lin. paper code
Meta-learning for fast cross-lingual adaptation in dependency parsing, in ACL, 2022. A. Langedijk, V. Dankers, P. Lippe, S. Bos, B. C. Guevara, H. Yannakoudakis, and E. Shutova. paper code
Enhancing cross-lingual natural language inference by prompt-learning from cross-lingual templates, in ACL, 2022. K. Qi, H. Wan, J. Du, and H. Chen. paper code
Few-shot stance detection via target-aware prompt distillation, in SIGIR, 2022. Y. Jiang, J. Gao, H. Shen, and X. Cheng. paper code
Relation-guided few-shot relational triple extraction, in SIGIR, 2022. X. Cong, J. Sheng, S. Cui, B. Yu, T. Liu, and B. Wang. paper
Curriculum contrastive context denoising for few-shot conversational dense retrieval, in SIGIR, 2022. K. Mao, Z. Dou, and H. Qian. paper code
Few-shot subgoal planning with language models, in NAACL, 2022. L. Logeswaran, Y. Fu, M. Lee, and H. Lee. paper
Template-free prompt tuning for few-shot NER, in NAACL, 2022. R. Ma, X. Zhou, T. Gui, Y. Tan, L. Li, Q. Zhang, and X. Huang. paper code
Few-shot document-level relation extraction, in NAACL, 2022. N. Popovic, and M. Färber. paper code
An enhanced span-based decomposition method for few-shot sequence labeling, in NAACL, 2022. P. Wang, R. Xu, T. Liu, Q. Zhou, Y. Cao, B. Chang, and Z. Sui. paper code
Automatic multi-label prompting: Simple and interpretable few-shot classification, in NAACL, 2022. H. Wang, C. Xu, and J. McAuley. paper code
On the effect of pretraining corpora on in-context few-shot learning by a large-scale language model, in NAACL, 2022. S. Shin, S.-W. Lee, H. Ahn, S. Kim, H. Kim, B. Kim, K. Cho, G. Lee, W. Park, J.-W. Ha, and N. Sung. paper
MGIMN: Multi-grained interactive matching network for few-shot text classification, in NAACL, 2022. J. Zhang, M. Maimaiti, G. Xing, Y. Zheng, and J. Zhang. paper
On the economics of multilingual few-shot learning: Modeling the cost-performance trade-offs of machine translated and manual data, in NAACL, 2022. K. Ahuja, M. Choudhury, and S. Dandapat. paper code
OmniTab: Pretraining with natural and synthetic data for few-shot table-based question answering, in NAACL, 2022. Z. Jiang, Y. Mao, P. He, G. Neubig, and W. Chen. paper code
Fine-tuning pre-trained language models for few-shot intent detection: Supervised pre-training and isotropization, in NAACL, 2022. H. Zhang, H. Liang, Y. Zhang, L.-M. Zhan, X.-M. Wu, X. Lu, and A. Y. Lam. paper code
Embedding hallucination for few-shot language fine-tuning, in NAACL, 2022. Y. Jian, C. Gao, and S. Vosoughi. paper code
Few-shot semantic parsing with language models trained on code, in NAACL, 2022. R. Shin, and B. V. Durme. paper
LEA: Meta knowledge-driven self-attentive document embedding for few-shot text classification, in NAACL, 2022. S. Hong, and T. Y. Jang. paper
Contrastive learning for prompt-based few-shot language learners, in NAACL, 2022. Y. Jian, C. Gao, and S. Vosoughi. paper code
Learn from relation information: Towards prototype representation rectification for few-shot relation extraction, in NAACL, 2022. Y. Liu, J. Hu, X. Wan, and T.-H. Chang. paper code
Efficient few-shot fine-tuning for opinion summarization, in NAACL, 2022. A. Brazinskas, R. Nallapati, M. Bansal, and M. Dreyer. paper code
Improving few-shot image classification using machine- and user-generated natural language descriptions, in NAACL, 2022. K. Nishida, K. Nishida, and S. Nishioka. paper
RGL: A simple yet effective relation graph augmented prompt-based tuning approach for few-shot learning, in NAACL, 2022. Y. Wang, X. Tian, H. Xiong, Y. Li, Z. Chen, S. Guo, and D. Dou. paper code
“Diversity and uncertainty in moderation” are the key to data selection for multilingual few-shot transfer, in NAACL, 2022. S. Kumar, S. Dandapat, and M. Choudhury. paper
A generative language model for few-shot aspect-based sentiment analysis, in NAACL, 2022. E. Hosseini-Asl, W. Liu, and C. Xiong. paper code
Improving few-shot relation classiﬁcation by prototypical representation learning with deﬁnition text, in NAACL, 2022. L. Zhenzhen, Y. Zhang, J.-Y. Nie, and D. Li. paper
Few-shot self-rationalization with natural language prompts, in NAACL, 2022. A. Marasovic, I. Beltagy, D. Downey, and M. E. Peters. paper code
How to translate your samples and choose your shots? Analyzing translate-train & few-shot cross-lingual transfer, in NAACL, 2022. I. Jundi, and G. Lapesa. paper code
LMTurk: Few-shot learners as crowdsourcing workers in a language-model-as-a-service framework, in NAACL, 2022. M. Zhao, F. Mi, Y. Wang, M. Li, X. Jiang, Q. Liu, and H. Schuetze. paper code
LiST: Lite prompted self-training makes efficient few-shot learners, in NAACL, 2022. Y. Wang, S. Mukherjee, X. Liu, J. Gao, A. H. Awadallah, and J. Gao. paper code
Improving in-context few-shot learning via self-supervised training, in NAACL, 2022. M. Chen, J. Du, R. Pasunuru, T. Mihaylov, S. Iyer, V. Stoyanov, and Z. Kozareva. paper
Por qué não utiliser alla språk? mixed training with gradient optimization in few-shot cross-lingual transfer, in NAACL, 2022. H. Xu, and K. Murray. paper code
On the effectiveness of sentence encoding for intent detection meta-learning, in NAACL, 2022. T. Ma, Q. Wu, Z. Yu, T. Zhao, and C.-Y. Lin. paper code
Few-shot fine-grained entity typing with automatic label interpretation and instance generation, in KDD, 2022. J. Huang, Y. Meng, and J. Han. paper code
Label-enhanced prototypical network with contrastive learning for multi-label few-shot aspect category detection, in KDD, 2022. H. Liu, F. Zhang, X. Zhang, S. Zhao, J. Sun, H. Yu, and X. Zhang. paper
Task-adaptive few-shot node classification, in KDD, 2022. S. Wang, K. Ding, C. Zhang, C. Chen, and J. Li. paper code
Diversity features enhanced prototypical network for few-shot intent detection, in IJCAI, 2022. F. Yang, X. Zhou, Y. Wang, A. Atawulla, and R. Bi. paper
Function-words adaptively enhanced attention networks for few-shot inverse relation classification, in IJCAI, 2022. C. Dou, S. Wu, X. Zhang, Z. Feng, and K. Wang. paper code
Curriculum-based self-training makes better few-shot learners for data-to-text generation, in IJCAI, 2022. P. Ke, H. Ji, Z. Yang, Y. Huang, J. Feng, X. Zhu, and M. Huang. paper code
Graph-based model generation for few-shot relation extraction, in EMNLP, 2022. W. Li, and T. Qian. paper code
Prompt-based meta-learning for few-shot text classification, in EMNLP, 2022. H. Zhang, X. Zhang, H. Huang, and L. Yu. paper code
Language models of code are few-shot commonsense learners, in EMNLP, 2022. A. Madaan, S. Zhou, U. Alon, Y. Yang, and G. Neubig. paper code
Large language models are few-shot clinical information extractors, in EMNLP, 2022. M. Agrawal, S. Hegselmann, H. Lang, Y. Kim, and D. Sontag. paper
ToKen: Task decomposition and knowledge infusion for few-shot hate speech detection, in EMNLP, 2022. B. AlKhamissi, F. Ladhak, S. Iyer, V. Stoyanov, Z. Kozareva, X. Li, P. Fung, L. Mathias, A. Celikyilmaz, and M. Diab. paper
Exploiting domain-slot related keywords description for few-shot cross-domain dialogue state tracking, in EMNLP, 2022. Q. Gao, G. Dong, Y. Mou, L. Wang, C. Zeng, D. Guo, M. Sun, and W. Xu. paper
KECP: Knowledge enhanced contrastive prompting for few-shot extractive question answering, in EMNLP, 2022. J. Wang, C. Wang, M. Qiu, Q. Shi, H. Wang, J. huang, and M. Gao. paper code
SpanProto: A two-stage span-based prototypical network for few-shot named entity recognition, in EMNLP, 2022. J. Wang, C. Wang, C. Tan, M. Qiu, S. Huang, J. huang, and M. Gao. paper code
Few-shot query-focused summarization with prefix-merging, in EMNLP, 2022. R. Yuan, Z. Wang, Z. Cao, and W. Li. paper
Incorporating relevance feedback for information-seeking retrieval using few-shot document re-ranking, in EMNLP, 2022. T. Baumgartner, L. F. R. Ribeiro, N. Reimers, and I. Gurevych. paper code
Few-shot learning with multilingual generative language models, in EMNLP, 2022. X. V. Lin, T. Mihaylov, M. Artetxe, T. Wang, S. Chen, D. Simig, M. Ott, N. Goyal, S. Bhosale, J. Du, R. Pasunuru, S. Shleifer, P. S. Koura, V. Chaudhary, B. O'Horo, J. Wang, L. Zettlemoyer, Z. Kozareva, M. Diab, V. Stoyanov, and X. Li. paper code
Don't stop fine-tuning: On training regimes for few-shot cross-lingual transfer with multilingual language models, in EMNLP, 2022. F. D. Schmidt, I. Vulic, and G. Glavas. paper code
Better few-shot relation extraction with label prompt dropout, in EMNLP, 2022. P. Zhang, and W. Lu. paper code
A dual prompt learning framework for few-shot dialogue state tracking, in WWW, 2023. Y. Yang, W. Lei, P. Huang, J. Cao, J. Li, and T.-S. Chua. paper code
MetaTroll: Few-shot detection of state-sponsored trolls with transformer adapters, in WWW, 2023. L. Tian, X. Zhang, and J. H. Lau. paper code
ContrastNet: A contrastive learning framework for few-shot text classification, in AAAI, 2022. J. Chen, R. Zhang, Y. Mao, and J. Xu. paper
Few-shot cross-lingual stance detection with sentiment-based pre-training, in AAAI, 2022. M. Hardalov, A. Arora, P. Nakov, and I. Augenstein. paper
ALP: Data augmentation using lexicalized PCFGs for few-shot text classification, in AAAI, 2022. H. H. Kim, D. Woo, S. J. Oh, J.-W. Cha, and Y.-S. Han. paper
CINS: Comprehensive instruction for few-shot learning in task-oriented dialog systems, in AAAI, 2022. F. Mi, Y. Wang, and Y. Li. paper
An empirical study of GPT-3 for few-shot knowledge-based VQA, in AAAI, 2022. Z. Yang, Z. Gan, J. Wang, X. Hu, Y. Lu, Z. Liu, and L. Wang. paper
PROMPTAGATOR: Few-shot dense retrieval from 8 examples, in ICLR, 2023. Z. Dai, V. Y. Zhao, J. Ma, Y. Luan, J. Ni, J. Lu, A. Bakalov, K. Guu, K. Hall, and M.-W. Chang. paper
QAID: Question answering inspired few-shot intent detection, in ICLR, 2023. A. Yehudai, M. Vetzler, Y. Mass, K. Lazar, D. Cohen, and B. Carmeli. paper
CLUR: Uncertainty estimation for few-shot text classification with contrastive learning, in KDD, 2023. J. He, X. Zhang, S. Lei, A. Alhamadani, F. Chen, B. Xiao, and C.-T. Lu. paper code
Learning few-shot sample-set operations for noisy multi-label aspect category detection, in IJCAI, 2023. S. Zhao, W. Chen, and T. Wang. paper
Few-shot document-level event argument extraction, in ACL, 2023. X. Yang, Y. Lu, and L. R. Petzold. paper code
FLamE: Few-shot learning from natural language explanations, in ACL, 2023. Y. Zhou, Y. Zhang, and C. Tan. paper
MetaAdapt: Domain adaptive few-shot misinformation detection via meta learning, in ACL, 2023. Z. Yue, H. Zeng, Y. Zhang, L. Shang, and D. Wang. paper code
Code4Struct: Code generation for few-shot event structure prediction, in ACL, 2023. X. Wang, S. Li, and H. Ji. paper code
MANNER: A variational memory-augmented model for cross domain few-shot named entity recognition, in ACL, 2023. J. Fang, X. Wang, Z. Meng, P. Xie, F. Huang, and Y. Jiang. paper code
Dual class knowledge propagation network for multi-label few-shot intent detection, in ACL, 2023. F. Zhang, W. Chen, F. Ding, and T. Wang. paper
Few-shot event detection: An empirical study and a unified view, in ACL, 2023. Y. Ma, Z. Wang, Y. Cao, and A. Sun. paper code
CodeIE: Large code generation models are better few-shot information extractors, in ACL, 2023. P. Li, T. Sun, Q. Tang, H. Yan, Y. Wu, X. Huang, and X. Qiu. paper code
Few-shot data-to-text generation via unified representation and multi-source learning, in ACL, 2023. A. H. Li, M. Shang, E. Spiliopoulou, J. Ma, P. Ng, Z. Wang, B. Min, W. Y. Wang, K. R. McKeown, V. Castelli, D. Roth, and B. Xiang. paper
Few-shot in-context learning on knowledge base question answering, in ACL, 2023. T. Li, X. Ma, A. Zhuang, Y. Gu, Y. Su, and W. Chen. paper code
Linguistic representations for fewer-shot relation extraction across domains, in ACL, 2023. S. Gururaja, R. Dutt, T. Liao, and C. P. Rosé. paper code
Few-shot reranking for multi-hop QA via language model prompting, in ACL, 2023. M. Khalifa, L. Logeswaran, M. Lee, H. Lee, and L. Wang. paper code
A domain-transfer meta task design paradigm for few-shot slot tagging, in AAAI, 2023. F. Yang, X. Zhou, Y. Yang, B. Ma, R. Dong, and A. Atawulla. paper
Revisiting sparse retrieval for few-shot entity linking, in EMNLP, 2023. Y. Chen, Z. Xu, B. Hu, and M. Zhang. paper code
Vicinal risk minimization for few-shot cross-lingual transfer in abusive language detection, in EMNLP, 2023. G. D. l. P. Sarracén, P. Rosso, R. Litschko, G. Glavaš, and S. Ponzetto. paper
Hypernetwork-based decoupling to improve model generalization for few-shot relation extraction, in EMNLP, 2023. L. Zhang, C. Zhou, F. Meng, J. Su, Y. Chen, and J. Zhou. paper code
Towards low-resource automatic program repair with meta-learning and pretrained language models, in EMNLP, 2023. W. Wang, Y. Wang, S. Hoi, and S. Joty. paper code
Few-shot detection of machine-generated text using style representations, in ICLR, 2024. R. A. R. Soto, K. Koch, A. Khan, B. Y. Chen, M. Bishop, and N. Andrews. paper

Knowledge Graph

MetaEXP: Interactive explanation and exploration of large knowledge graphs, in WWW, 2018. F. Behrens, S. Bischoff, P. Ladenburger, J. Rückin, L. Seidel, F. Stolp, M. Vaichenker, A. Ziegler, D. Mottin, F. Aghaei, E. Müller, M. Preusse, N. Müller, and M. Hunger. paper code
Meta relational learning for few-shot link prediction in knowledge graphs, in EMNLP-IJCNLP, 2019. M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen. paper
Adapting meta knowledge graph information for multi-hop reasoning over few-shot relations, in EMNLP-IJCNLP, 2019. X. Lv, Y. Gu, X. Han, L. Hou, J. Li, and Z. Liu. paper
Knowledge graph transfer network for few-shot recognition, in AAAI, 2020. R. Chen, T. Chen, X. Hui, H. Wu, G. Li, and L. Lin. paper
Few-shot knowledge graph completion, in AAAI, 2020. C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla. paper
Adaptive attentional network for few-shot knowledge graph completion, in EMNLP, 2020. J. Sheng, S. Guo, Z. Chen, J. Yue, L. Wang, T. Liu, and H. Xu. paper code
Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion, in SIGIR, 2021. G. Niu, Y. Li, C. Tang, R. Geng, J. Dai, Q. Liu, H. Wang, J. Sun, F. Huang, and L. Si. paper
Learning inter-entity-interaction for few-shot knowledge graph completion, in EMNLP, 2022. Y. Li, K. Yu, X. Huang, and Y. Zhang. paper code
Meta-learning based knowledge extrapolation for temporal knowledge graph, in WWW, 2023. Z. Chen, C. Xu, F. Su, Z. Huang, and Y. Dou. paper
Learning to sample and aggregate: Few-shot reasoning over temporal knowledge graphs, in NeurIPS, 2022. R. Wang, z. li, D. Sun, S. Liu, J. Li, B. Yin, and T. Abdelzaher. paper
Few-shot relational reasoning via connection subgraph pretraining, in NeurIPS, 2022. Q. Huang, H. Ren, and J. Leskovec. paper code
Hierarchical relational learning for few-shot knowledge graph completion, in ICLR, 2023. H. Wu, J. Yin, B. Rajaratnam, and J. Guo. paper code
The unreasonable effectiveness of few-shot learning for machine translation, in ICML, 2023. X. Garcia, Y. Bansal, C. Cherry, G. F. Foster, M. Krikun, M. Johnson, and O. Firat. paper
Prompting large language models with chain-of-thought for few-shot knowledge base question generation, in EMNLP, 2023. Y. Liang, J. Wang, H. Zhu, L. Wang, W. Qian, and Y. Lan. paper

Acoustic Signal Processing

One-shot learning of generative speech concepts, in CogSci, 2014. B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum. paper
Machine speech chain with one-shot speaker adaptation, INTERSPEECH, 2018. A. Tjandra, S. Sakti, and S. Nakamura. paper
Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion, INTERSPEECH, 2018. S. H. Mohammadi and T. Kim. paper
Few-shot audio classification with attentional graph neural networks, INTERSPEECH, 2019. S. Zhang, Y. Qin, K. Sun, and Y. Lin. paper
One-shot voice conversion with disentangled representations by leveraging phonetic posteriorgrams, INTERSPEECH, 2019. S. H. Mohammadi, and T. Kim. paper
One-shot voice conversion with global speaker embeddings, INTERSPEECH, 2019. H. Lu, Z. Wu, D. Dai, R. Li, S. Kang, J. Jia, and H. Meng. paper
One-shot voice conversion by separating speaker and content representations with instance normalization, INTERSPEECH, 2019. J.-C. Chou, and H.-Y. Lee. paper
Audio2Head: Audio-driven one-shot talking-head generation with natural head motion, in IJCAI, 2021. S. Wang, L. Li, Y. Ding, C. Fan, and X. Yu. paper
Few-shot low-resource knowledge graph completion with multi-view task representation generation, in KDD, 2023. S. Pei, Z. Kou, Q. Zhang, and X. Zhang. paper code
Normalizing flow-based neural process for few-shot knowledge graph completion, in SIGIR, 2023. L. Luo, Y.-F. Li, G. Haffari, and S. Pan. paper code

Recommendation

A meta-learning perspective on cold-start recommendations for items, in NeurIPS, 2017. M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle. paper
MeLU: Meta-learned user preference estimator for cold-start recommendation, in KDD, 2019. H. Lee, J. Im, S. Jang, H. Cho, and S. Chung. paper code
Sequential scenario-specific meta learner for online recommendation, in KDD, 2019. Z. Du, X. Wang, H. Yang, J. Zhou, and J. Tang. paper code
Few-shot learning for new user recommendation in location-based social networks, in WWW, 2020. R. Li, X. Wu, X. Chen, and W. Wang. paper
MAMO: Memory-augmented meta-optimization for cold-start recommendation, in KDD, 2020. M. Dong, F. Yuan, L. Yao, X. Xu, and L. Zhu. paper code
Meta-learning on heterogeneous information networks for cold-start recommendation, in KDD, 2020. Y. Lu, Y. Fang, and C. Shi. paper code
MetaSelector: Meta-learning for recommendation with user-level adaptive model selection, in WWW, 2020. M. Luo, F. Chen, P. Cheng, Z. Dong, X. He, J. Feng, and Z. Li. paper
Fast adaptation for cold-start collaborative filtering with meta-learning, in ICDM, 2020. T. Wei, Z. Wu, R. Li, Z. Hu, F. Feng, X. H. Sun, and W. Wang. paper
Preference-adaptive meta-learning for cold-start recommendation, in IJCAI, 2021. L. Wang, B. Jin, Z. Huang, H. Zhao, D. Lian, Q. Liu, and E. Chen. paper
Meta-learning helps personalized product search, in WWW, 2022. B. Wu, Z. Meng, Q. Zhang, and S. Liang. paper
Alleviating cold-start problem in CTR prediction with a variational embedding learning framework, in WWW, 2022. X. Xu, C. Yang, Q. Yu, Z. Fang, J. Wang, C. Fan, Y. He, C. Peng, Z. Lin, and J. Shao. paper
PNMTA: A pretrained network modulation and task adaptation approach for user cold-start recommendation, in WWW, 2022. H. Pang, F. Giunchiglia, X. Li, R. Guan, and X. Feng. paper
Few-shot news recommendation via cross-lingual transfer, in WWW, 2023. T. Guo, L. Yu, B. Shihada, and X. Zhang. paper code
ColdNAS: Search to modulate for user cold-start recommendation, in WWW, 2023. S. Wu, Y. Wang, Q. Jing, D. Dong, D. Dou, and Q. Yao. paper code
Contrastive collaborative filtering for cold-start item recommendation, in WWW, 2023. Z. Zhou, L. Zhang, and N. Yang. paper code
Bootstrapping contrastive learning enhanced music cold-start matching, in WWW, 2023. X. Zhao, Y. Zhang, Q. Xiao, Y. Ren, and Y. Yang. paper
A dynamic meta-learning model for time-sensitive cold-start recommendations, in AAAI, 2022. K. P. Neupane, E. Zheng, Y. Kong, and Q. Yu. paper
SMINet: State-aware multi-aspect interests representation network for cold-start users recommendation, in AAAI, 2022. W. Tao, Y. Li, L. Li, Z. Chen, H. Wen, P. Chen, T. Liang, and Q. Lu. paper code
Multimodality helps unimodality: Cross-modal few-shot learning with multimodal models, in CVPR, 2023. Z. Lin, S. Yu, Z. Kuang, D. Pathak, and D. Ramanan. paper code
M2EU: Meta learning for cold-start recommendation via enhancing user preference estimation, in SIGIR, 2023. Z. Wu, and X. Zhou. paper code
TAML: Time-aware meta learning for cold-start problem in news recommendation, in SIGIR, 2023. J. Li, Y. Zhang, X. Lin, X. Yang, G. Zhou, L. Li, H. Chen, and J. Zhou. paper
Uncertainty-aware consistency learning for cold-start item recommendation, in SIGIR, 2023. T. Liu, C. Gao, Z. Wang, D. Li, J. Hao, D. Jin, and Y. Li. paper
DCBT: A simple but effective way for unified warm and cold recommendation, in SIGIR, 2023. J. Yang, L. Zhang, Y. He, K. Ding, Z. Huan, X. Zhang, and L. Mo. paper
A preference learning decoupling framework for user cold-start recommendation, in SIGIR, 2023. C. Wang, Y. Zhu, A. Sun, Z. Wang, and K. Wang. paper
Aligning distillation for cold-start item recommendation, in SIGIR, 2023. F. Huang, Z. Wang, X. Huang, Y. Qian, Z. Li, and H. Chen. paper code

Others

Low data drug discovery with one-shot learning, ACS Central Science, 2017. H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande. paper
SMASH: One-shot model architecture search through hypernetworks, in ICLR, 2018. A. Brock, T. Lim, J. Ritchie, and N. Weston. paper
SPARC: Self-paced network representation for few-shot rare category characterization, in KDD, 2018. D. Zhou, J. He, H. Yang, and W. Fan. paper
MetaPred: Meta-learning for clinical risk prediction with limited patient electronic health records, in KDD, 2019. X. S. Zhang, F. Tang, H. H. Dodge, J. Zhou, and F. Wang. paper code
AffnityNet: Semi-supervised few-shot learning for disease type prediction, in AAAI, 2019. T. Ma, and A. Zhang. paper
Data augmentation using learned transformations for one-shot medical image segmentation, in CVPR, 2019. A. Zhao, G. Balakrishnan, F. Durand, J. V. Guttag, and A. V. Dalca. paper
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction, in WWW, 2019. H. Yao, Y. Liu, Y. Wei, X. Tang, and Z. Li. paper code
Federated meta-learning for fraudulent credit card detection, in IJCAI, 2020. W. Zheng, L. Yan, C. Gou, and F. Wang. paper
Differentially private meta-learning, in ICLR, 2020. J. Li, M. Khodak, S. Caldas, and A. Talwalkar. paper
Towards fast adaptation of neural architectures with meta learning, in ICLR, 2020. D. Lian, Y. Zheng, Y. Xu, Y. Lu, L. Lin, P. Zhao, J. Huang, and S. Gao. paper
LT-Net: Label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation, in CVPR, 2020. S. Wang, S. Cao, D. Wei, R. Wang, K. Ma, L. Wang, D. Meng, and Y. Zheng. paper
Few-shot pill recognition, in CVPR, 2020. S. Ling, A. Pastor, J. Li, Z. Che, J. Wang, J. Kim, and P. L. Callet. paper
Self-supervision with superpixels: Training few-shot medical image segmentation without annotation, in ECCV, 2020. C. Ouyang, C. Biffi, C. Chen, T. Kart, H. Qiu, and D. Rueckert. paper code
Deep complementary joint model for complex scene registration and few-shot segmentation on medical images, in ECCV, 2020. Y. He, T. Li, G. Yang, Y. Kong, Y. Chen, H. Shu, J. Coatrieux, J. Dillenseger, and S. Li. paper
Using optimal embeddings to learn new intents with few examples: An application in the insurance domain, in KDD, 2020. S. Acharya, and G. Fung. paper
Meta-learning for query conceptualization at web scale, in KDD, 2020. F. X. Han, D. Niu, H. Chen, W. Guo, S. Yan, and B. Long. paper
Few-sample and adversarial representation learning for continual stream mining, in WWW, 2020. Z. Wang, Y. Wang, Y. Lin, E. Delord, and L. Khan. paper
Few-shot graph learning for molecular property prediction, in WWW, 2021. Z. Guo, C. Zhang, W. Yu, J. Herr, O. Wiest, M. Jiang, and N. V. Chawla. paper code
Taxonomy-aware learning for few-shot event detection, in WWW, 2021. J. Zheng, F. Cai, W. Chen, W. Lei, and H. Chen. paper
Learning from graph propagation via ordinal distillation for one-shot automated essay scoring, in WWW, 2021. Z. Jiang, M. Liu, Y. Yin, H. Yu, Z. Cheng, and Q. Gu. paper
Few-shot network anomaly detection via cross-network meta-learning, in WWW, 2021. K. Ding, Q. Zhou, H. Tong, and H. Liu. paper
Few-shot knowledge validation using rules, in WWW, 2021. M. Loster, D. Mottin, P. Papotti, J. Ehmüller, B. Feldmann, and F. Naumann. paper
Graph learning regularization and transfer learning for few-shot event detection, in SIGIR, 2021. V. D. Lai, M. V. Nguyen, T. H. Nguyen, and F. Dernoncourt. paper code
Graph-evolving meta-learning for low-resource medical dialogue generation, in AAAI, 2021. S. Lin, P. Zhou, X. Liang, J. Tang, R. Zhao, Z. Chen, and L. Lin. paper
Modeling the probabilistic distribution of unlabeled data for one-shot medical image segmentation, in AAAI, 2021. Y. Ding, X. Yu, and Y. Yang. paper code
Progressive network grafting for few-shot knowledge distillation, in AAAI, 2021. C. Shen, X. Wang, Y. Yin, J. Song, S. Luo, and M. Song. paper code
Curriculum meta-learning for next POI recommendation, in KDD, 2021. Y. Chen, X. Wang, M. Fan, J. Huang, S. Yang, and W. Zhu. paper code
MFNP: A meta-optimized model for few-shot next POI recommendation, in IJCAI, 2021. H. Sun, J. Xu, K. Zheng, P. Zhao, P. Chao, and X. Zhou. paper
Physics-aware spatiotemporal modules with auxiliary tasks for meta-learning, in IJCAI, 2021. S. Seo, C. Meng, S. Rambhatla, and Y. Liu. paper
Recurrent mask refinement for few-shot medical image segmentation, in ICCV, 2021. H. Tang, X. Liu, S. Sun, X. Yan, and X. Xie. paper code
Property-aware relation networks for few-shot molecular property prediction, in NeurIPS, 2021. Y. Wang, A. Abuduweili, Q. Yao, and D. Dou. paper code
Few-shot data-driven algorithms for low rank approximation, in NeurIPS, 2021. P. Indyk, T. Wagner, and D. Woodruff. paper
Non-Gaussian Gaussian processes for few-shot regression, in NeurIPS, 2021. M. Sendera, J. Tabor, A. Nowak, A. Bedychaj, M. Patacchiola, T. Trzcinski, P. Spurek, and M. Zieba. paper
HELP: Hardware-adaptive efficient latency prediction for NAS via meta-learning, in NeurIPS, 2021. H. Lee, S. Lee, S. Chong, and S. J. Hwang. paper
Learning to learn dense Gaussian processes for few-shot learning, in NeurIPS, 2021. Z. Wang, Z. Miao, X. Zhen, and Q. Qiu. paper
A meta-learning based stress category detection framework on social media, in WWW, 2022. X. Wang, L. Cao, H. Zhang, L. Feng, Y. Ding, and N. Li. paper
Which images to label for few-shot medical landmark detection?, in CVPR, 2022. Q. Quan, Q. Yao, J. Li, and S. K. Zhou. paper
Recognizing medical search query intent by few-shot learning, in SIGIR, 2022. Y. Wang, S. Wang, L. Yanyan, and D. Dou. paper code
MetaCare++: Meta-learning with hierarchical subtyping for cold-start diagnosis prediction in healthcare data, in SIGIR, 2022. Y. Tan, C. Yang, X. Wei, C. Chen, W. Liu, L. Li, and J. Z. a. X. Zheng. paper
Spatio-temporal graph few-shot learning with cross-city knowledge transfer, in KDD, 2022. B. Lu, X. Gan, W. Zhang, H. Yao, L. Fu, and X. Wang. paper code
Few-shot learning for trajectory-based mobile game cheating detection, in KDD, 2022. Y. Su, D. Yao, X. Chu, W. Li, J. Bi, S. Zhao, R. Wu, S. Zhang, J. Tao, and H. Deng. paper code
Improving few-shot text-to-SQL with meta self-training via column specificity, in IJCAI, 2022. X. Guo, Y. Chen, G. Qi, T. Wu, and H. Xu. paper code
Graph few-shot learning with task-specific structures, in NeurIPS, 2022. S. Wang, C. Chen, and J. Li. paper code
Meta-learning dynamics forecasting using task inference, in NeurIPS, 2022. R. Wang, R. Walters, and R. Yu. paper code
Rapid model architecture adaption for meta-learning, in NeurIPS, 2022. Y. Zhao, X. Gao, I. Shumailov, N. Fusi, and R. D. Mullins. paper
Cross-domain few-shot graph classification, in AAAI, 2022. and K. Hassani. paper
Meta propagation networks for graph few-shot semi-supervised learning, in AAAI, 2022. K. Ding, J. Wang, J. Caverlee, and H. Liu. paper code
Pushing the limits of few-shot anomaly detection in industry vision: GraphCore, in ICLR, 2023. G. Xie, J. Wang, J. Liu, F. Zheng, and Y. Jin. paper
Context-enriched molecule representations improve few-shot drug discovery, in ICLR, 2023. J. Schimunek, P. Seidl, L. Friedrich, D. Kuhn, F. Rippmann, S. Hochreiter, and G. Klambauer. paper code
Sequential latent variable models for few-shot high-dimensional time-series forecasting, in ICLR, 2023. X. Jiang, R. Missel, Z. Li, and L. Wang. paper code
Transfer NAS with meta-learned Bayesian surrogates, in ICLR, 2023. G. Shala, T. Elsken, F. Hutter, and J. Grabocka. paper code
Few-shot domain adaptation for end-to-end communication, in ICLR, 2023. J. Raghuram, Y. Zeng, D. Garcia, R. Ruiz, S. Jha, J. Widmer, and S. Banerjee. paper code
Rethinking few-shot medical segmentation: A vector quantization view, in CVPR, 2023. S. Huang, T. Xu, N. Shen, F. Mu, and J. Li. paper
Virtual node tuning for few-shot node classification, in KDD, 2023. Z. Tan, R. Guo, K. Ding, and H. Liu. paper
Contrastive meta-learning for few-shot node classification, in KDD, 2023. S. Wang, Z. Tan, H. Liu, and J. Li. paper code
Task-equivariant graph few-shot learning, in KDD, 2023. S. Kim, J. Lee, N. Lee, W. Kim, S. Choi, and C. Park. paper code
Leveraging transferable knowledge concept graph embedding for cold-start cognitive diagnosis, in SIGIR, 2023. W. Gao, H. Wang, Q. Liu, F. Wang, X. Lin, L. Yue, Z. Zhang, R. Lv, and S. Wang. paper code
Dual meta-learning with longitudinally consistent regularization for one-shot brain tissue segmentation across the human lifespan, in ICCV, 2023. Y. Sun, F. Wang, J. Shu, H. Wang, L. Wang, D. Meng, and C. Lian. paper code
The rise of AI language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification, in NeurIPS, 2023. L. Qu, x. Luo, K. Fu, M. Wang, and Z. Song. paper code
Robust one-shot segmentation of brain tissues via image-aligned style transformation, in AAAI, 2023. J. Lv, X. Zeng, S. Wang, R. Duan, Z. Wang, and Q. Li. paper code
Supervised contrastive few-shot learning for high-frequency time series, in AAAI, 2023. X. Chen, C. Ge, M. Wang, and J. Wang. paper
Cross-domain few-shot graph classification with a reinforced task coordinator, in AAAI, 2023. Q. Zhang, S. Pei, Q. Yang, C. Zhang, N. V. Chawla, and X. Zhang. paper
Robust graph meta-learning via manifold calibration with proxy subgraphs, in AAAI, 2023. Z. Wang, L. Cao, W. Lin, M. Jiang, and K. C. Tan. paper
Few-shot defect image generation via defect-aware feature manipulation, in AAAI, 2023. Y. Duan, Y. Hong, L. Niu, and L. Zhang. paper code
Multi-label few-shot ICD coding as autoregressive generation with prompt, in AAAI, 2023. Z. Yang, S. Kwon, Z. Yao, and H. Yu. paper code
Enhancing small medical learners with privacy-preserving contextual prompting, in ICLR, 2024. X. Zhang, S. Li, X. Yang, C. Tian, Y. Qin, and L. R. Petzol. paper code
TEST: Text prototype aligned embedding to activate LLM's ability for time series, in ICLR, 2024. C. Sun, Y. Li, H. Li, and S. Hong. paper

Theories

Learning to learn around a common mean, in NeurIPS, 2018. G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil. paper
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm, in ICLR, 2018. C. Finn and S. Levine. paper
A theoretical analysis of the number of shots in few-shot learning, in ICLR, 2020. T. Cao, M. T. Law, and S. Fidler. paper
Rapid learning or feature reuse? Towards understanding the effectiveness of MAML, in ICLR, 2020. A. Raghu, M. Raghu, S. Bengio, and O. Vinyals. paper
Robust meta-learning for mixed linear regression with small batches, in NeurIPS, 2020. W. Kong, R. Somani, S. Kakade, and S. Oh. paper
One-shot distributed ridge regression in high dimensions, in ICML, 2020. Y. Sheng, and E. Dobriban. paper
Bridging the gap between practice and PAC-Bayes theory in few-shot meta-learning, in NeurIPS, 2021. N. Ding, X. Chen, T. Levinboim, S. Goodman, and R. Soricut. paper
Generalization bounds for meta-learning: An information-theoretic analysis, in NeurIPS, 2021. Q. CHEN, C. Shui, and M. Marchand. paper
Generalization bounds for meta-learning via PAC-Bayes and uniform stability, in NeurIPS, 2021. A. Farid, and A. Majumdar. paper
Unraveling model-agnostic meta-learning via the adaptation learning rate, in ICLR, 2022. Y. Zou, F. Liu, and Q. Li. paper
On the importance of firth bias reduction in few-shot classification, in ICLR, 2022. S. Ghaffari, E. Saleh, D. Forsyth, and Y. Wang. paper code
Global convergence of MAML and theory-inspired neural architecture search for few-shot learning, in CVPR, 2022. H. Wang, Y. Wang, R. Sun, and B. Li. paper
Smoothed embeddings for certified few-shot learning, in NeurIPS, 2022. M. Pautov, O. Kuznetsova, N. Tursynbek, A. Petiushko, and I. Oseledets. paper code
Towards few-shot adaptation of foundation models via multitask finetuning, in ICLR, 2024. Z. Xu, Z. Shi, J. Wei, F. Mu, Y. Li, and Y. Liang. paper code

Few-shot Learning and Zero-shot Learning

Label-embedding for attribute-based classification, in CVPR, 2013. Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid. paper
A unified semantic embedding: Relating taxonomies and attributes, in NeurIPS, 2014. S. J. Hwang and L. Sigal. paper
Multi-attention network for one shot learning, in CVPR, 2017. P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. T. Shen. paper
Few-shot and zero-shot multi-label learning for structured label spaces, in EMNLP, 2018. A. Rios and R. Kavuluru. paper
Learning compositional representations for few-shot recognition, in ICCV, 2019. P. Tokmakov, Y.-X. Wang, and M. Hebert. paper code
Large-scale few-shot learning: Knowledge transfer with class hierarchy, in CVPR, 2019. A. Li, T. Luo, Z. Lu, T. Xiang, and L. Wang. paper
Generalized zero- and few-shot learning via aligned variational autoencoders, in CVPR, 2019. E. Schonfeld, S. Ebrahimi, S. Sinha, T. Darrell, and Z. Akata. paper code
F-VAEGAN-D2: A feature generating framework for any-shot learning, in CVPR, 2019. Y. Xian, S. Sharma, B. Schiele, and Z. Akata. paper
TGG: Transferable graph generation for zero-shot and few-shot learning, in ACM MM, 2019. C. Zhang, X. Lyu, and Z. Tang. paper
Adaptive cross-modal few-shot learning, in NeurIPS, 2019. C. Xing, N. Rostamzadeh, B. N. Oreshkin, and P. O. Pinheiro. paper
Learning meta model for zero- and few-shot face anti-spoofing, in AAAI, 2020. Y. Qin, C. Zhao, X. Zhu, Z. Wang, Z. Yu, T. Fu, F. Zhou, J. Shi, and Z. Lei. paper
RD-GAN: Few/Zero-shot chinese character style transfer via radical decomposition and rendering, in ECCV, 2020. Y. Huang, M. He, L. Jin, and Y. Wang. paper
An empirical study on large-scale multi-label text classification including few and zero-shot labels, in EMNLP, 2020. I. Chalkidis, M. Fergadiotis, S. Kotitsas, P. Malakasiotis, N. Aletras, and I. Androutsopoulos. paper
Multi-label few/zero-shot learning with knowledge aggregated from multiple label graphs, in EMNLP, 2020. J. Lu, L. Du, M. Liu, and J. Dipnall. paper
Emergent complexity and zero-shot transfer via unsupervised environment design, in NeurIPS, 2020. M. Dennis, N. Jaques, E. Vinitsky, A. Bayen, S. Russell, A. Critch, and S. Levine. paper
Learning graphs for knowledge transfer with limited labels, in CVPR, 2021. P. Ghosh, N. Saini, L. S. Davis, and A. Shrivastava. paper
Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation, in NAACL-HLT, 2021. A. R. Fabbri, S. Han, H. Li, H. Li, M. Ghazvininejad, S. R. Joty, D. R. Radev, and Y. Mehdad. paper
SEQZERO: Few-shot compositional semantic parsing with sequential prompts and zero-shot models, in NAACL, 2022. J. Yang, H. Jiang, Q. Yin, D. Zhang, B. Yin, and D. Yang. paper code
Label verbalization and entailment for effective zero and few-shot relation extraction, in EMNLP, 2021. O. Sainz, O. L. d. Lacalle, G. Labaka, A. Barrena, and E. Agirre. paper code
An empirical investigation of word alignment supervision for zero-shot multilingual neural machine translation, in EMNLP, 2021. A. Raganato, R. Vázquez, M. Creutz, and J. Tiedemann. paper
Bridge to target domain by prototypical contrastive learning and label confusion: Re-explore zero-shot learning for slot filling, in EMNLP, 2021. L. Wang, X. Li, J. Liu, K. He, Y. Yan, and W. Xu. paper code
A label-aware BERT attention network for zero-shot multi-intent detection in spoken language understanding, in EMNLP, 2021. T. Wu, R. Su, and B. Juang. paper
Zero-shot dialogue disentanglement by self-supervised entangled response selection, in EMNLP, 2021. T. Chi, and A. I. Rudnicky. paper code
Robust retrieval augmented generation for zero-shot slot filling, in EMNLP, 2021. M. R. Glass, G. Rossiello, M. F. M. Chowdhury, and A. Gliozzo. paper code
Everything is all it takes: A multipronged strategy for zero-shot cross-lingual information extraction, in EMNLP, 2021. M. Yarmohammadi, S. Wu, M. Marone, H. Xu, S. Ebner, G. Qin, Y. Chen, J. Guo, C. Harman, K. Murray, A. S. White, M. Dredze, and B. V. Durme. paper code
An empirical study on multiple information sources for zero-shot fine-grained entity typing, in EMNLP, 2021. Y. Chen, H. Jiang, L. Liu, S. Shi, C. Fan, M. Yang, and R. Xu. paper
Zero-shot dialogue state tracking via cross-task transfer, in EMNLP, 2021. Z. Lin, B. Liu, A. Madotto, S. Moon, Z. Zhou, P. Crook, Z. Wang, Z. Yu, E. Cho, R. Subba, and P. Fung. paper code
Finetuned language models are zero-shot learners, in ICLR, 2022. J. Wei, M. Bosma, V. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le. paper code
Zero-shot stance detection via contrastive learning, in WWW, 2022. B. Liang, Z. Chen, L. Gui, Y. He, M. Yang, and R. Xu. paper code
Reframing instructional prompts to GPTk's language, in Findings of ACL, 2022. D. Khashabi, C. Baral, Y. Choi, and H. Hajishirzi. paper
JointCL: A joint contrastive learning framework for zero-shot stance detection, in ACL, 2022. B. Liang, Q. Zhu, X. Li, M. Yang, L. Gui, Y. He, and R. Xu. paper code
Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification, in ACL, 2022. S. Hu, N. Ding, H. Wang, Z. Liu, J. Wang, J. Li, W. Wu, and M. Sun. paper code
Uni-Perceiver: Pre-training unified architecture for generic perception for zero-shot and few-shot tasks, in CVPR, 2022. X. Zhu, J. Zhu, H. Li, X. Wu, H. Li, X. Wang, and J. Dai. paper
Enhancing zero-shot stance detection via targeted background knowledge, in SIGIR, 2022. Q. Zhu, B. Liang, J. Sun, J. Du, and L. Z. a. X. Ruifeng. paper
Textual entailment for event argument extraction: Zero- and few-shot with multi-source learning, in NAACL, 2022. O. Sainz, I. Gonzalez-Dios, O. L. d. Lacalle, B. Min, and E. Agirre. paper code
Extreme zero-shot learning for extreme text classification, in NAACL, 2022. Y. Xiong, W.-C. Chang, C.-J. Hsieh, H.-F. Yu, and I. S. Dhillon. paper code
Domain-oriented prefix-tuning: Towards efficient and generalizable fine-tuning for zero-shot dialogue summarization, in NAACL, 2022. L. Zhao, F. Zheng, W. Zeng, K. He, W. Xu, H. Jiang, W. Wu, and Y. Wu. paper code
Nearest neighbor zero-shot inference, in EMNLP, 2022. W. Shi, J. Michael, S. Gururangan, and L. Zettlemoyer. paper code
Continued pretraining for better zero- and few-shot promptability, in EMNLP, 2022. Z. Wu, R. L. L. IV, P. Walsh, A. Bhagia, D. Groeneveld, S. Singh, and I. Beltagy. paper code
InstructDial: Improving zero and few-shot generalization in dialogue through instruction tuning, in EMNLP, 2022. P. Gupta, C. Jiao, Y.-T. Yeh, S. Mehri, M. Eskenazi, and J. P. Bigham. paper code
Prompt-and-Rerank: A method for zero-shot and few-shot arbitrary textual style transfer with small language models, in EMNLP, 2022. M. Suzgun, L. Melas-Kyriazi, and D. Jurafsky. paper code
Learning instructions with unlabeled data for zero-shot cross-task generalization, in EMNLP, 2022. Y. Gu, P. Ke, X. Zhu, and M. Huang. paper code
Zero-shot cross-lingual transfer of prompt-based tuning with a unified multilingual prompt, in EMNLP, 2022. L. Huang, S. Ma, D. Zhang, F. Wei, and H. Wang. paper code
Finetune like you pretrain: Improved finetuning of zero-shot vision models, in CVPR, 2023. S. Goyal, A. Kumar, S. Garg, Z. Kolter, and A. Raghunathan. paper code
WinCLIP: Zero-/few-shot anomaly classification and segmentation, in CVPR, 2023. J. Jeong, Y. Zou, T. Kim, D. Zhang, A. Ravichandran, and O. Dabeer. paper
SemSup-XC: Semantic supervision for zero and few-shot extreme classification, in ICML, 2023. P. Aggarwal, A. Deshpande, and K. R. Narasimhan. paper code
Zero- and few-shot event detection via prompt-based meta learning, in ACL, 2023. Z. Yue, H. Zeng, M. Lan, H. Ji, and D. Wang. paper code
HINT: Hypernetwork instruction tuning for efficient zero- and few-shot generalisation, in ACL, 2023. H. Ivison, A. Bhagia, Y. Wang, H. Hajishirzi, and M. E. Peters. paper code
What does the failure to reason with "respectively" in zero/few-shot settings tell us about language models? acl 2023, in ACL, 2023. R. Cui, S. Lee, D. Hershcovich, and A. Søgaard. paper code
Pre-training intent-aware encoders for zero- and few-shot intent classification, in EMNLP, 2023. M. Sung, J. Gung, E. Mansimov, N. Pappas, R. Shu, S. Romeo, Y. Zhang, and V. Castelli. paper
ZGUL: Zero-shot generalization to unseen languages using multi-source ensembling of language adapters, in EMNLP, 2023. V. Rathore, R. Dhingra, P. Singla, and Mausam. paper code
Adaptive end-to-end metric learning for zero-shot cross-domain slot filling, in EMNLP, 2023. Y. Shi, L. Wu, and M. Shao. paper code
Empirical study of zero-shot NER with ChatGPT, in EMNLP, 2023. T. Xie, Q. Li, J. Zhang, Y. Zhang, Z. Liu, and H. Wang. paper code
Learning to describe for predicting zero-shot drug-drug interactions, in EMNLP, 2023. F. Zhu, Y. Zhang, L. Chen, B. Qin, and R. Xu. paper code
The benefits of label-description training for zero-shot text classification, in EMNLP, 2023. L. Gao, D. Ghosh, and K. Gimpel. paper code
Gen-Z: Generative zero-shot text classification with contextualized label descriptions, in ICLR, 2024. S. Kumar, C. Y. Park, and Y. Tsvetkov. paper
Evaluating the zero-shot robustness of instruction-tuned language models, in ICLR, 2024. J. Sun, C. Shaib, and B. C. Wallace. paper
Boosting prompting mechanisms for zero-shot speech synthesis, in ICLR, 2024. Z. Jiang, J. Liu, Y. Ren, J. He, Z. Ye, S. Ji, Q. Yang, C. Zhang, P. Wei, C. Wang, X. Yin, Z. MA, and Z. Zhao. paper
Zero and few-shot semantic parsing with ambiguous inputs, in ICLR, 2024. E. Stengel-Eskin, K. Rawlins, and B. V. Durme. paper
Uni3D: Exploring unified 3D representation at scale, in ICLR, 2024. J. Zhou, J. Wang, B. Ma, Y.-S. Liu, T. Huang, and X. Wang. paper

Variants of Few-shot Learning

Continuous adaptation via meta-learning in nonstationary and competitive environments, in ICLR, 2018. M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel. paper
Deep online learning via meta-learning: Continual adaptation for model-based RL, in ICLR, 2018. A. Nagabandi, C. Finn, and S. Levine. paper
Incremental few-shot learning with attention attractor networks, in NeurIPS, 2019. M. Ren, R. Liao, E. Fetaya, and R. S. Zemel. paper code
Bidirectional one-shot unsupervised domain mapping, in ICCV, 2019. T. Cohen, and L. Wolf. paper
XtarNet: Learning to extract task-adaptive representation for incremental few-shot learning, in ICML, 2020. S. W. Yoon, D. Kim, J. Seo, and J. Moon. paper code
Few-shot class-incremental learning, in CVPR, 2020. X. Tao, X. Hong, X. Chang, S. Dong, X. Wei, and Y. Gong. paper
Wandering within a world: Online contextualized few-shot learning, in ICLR, 2021. M. Ren, M. L. Iuzzolino, M. C. Mozer, and R. Zemel. paper
Repurposing pretrained models for robust out-of-domain few-shot learning, in ICLR, 2021. N. Kwon, H. Na, G. Huang, and S. Lacoste-Julien. paper code
Prototypical cross-domain self-supervised learning for few-shot unsupervised domain adaptation, in CVPR, 2021. X. Yue, Z. Zheng, S. Zhang, Y. Gao, T. Darrell, K. Keutzer, and A. S. Vincentelli. paper
Self-promoted prototype refinement for few-shot class-incremental learning, in CVPR, 2021. K. Zhu, Y. Cao, W. Zhai, J. Cheng, and Z. Zha. paper
Semantic-aware knowledge distillation for few-shot class-incremental learning, in CVPR, 2021. A. Cheraghian, S. Rahman, P. Fang, S. K. Roy, L. Petersson, and M. Harandi. paper
Few-shot incremental learning with continually evolved classifiers, in CVPR, 2021. C. Zhang, N. Song, G. Lin, Y. Zheng, P. Pan, and Y. Xu. paper
Learning a universal template for few-shot dataset generalization, in ICML, 2021. E. Triantafillou, H. Larochelle, R. Zemel, and V. Dumoulin. paper
GP-Tree: A gaussian process classifier for few-shot incremental learning, in ICML, 2021. I. Achituve, A. Navon, Y. Yemini, G. Chechik, and E. Fetaya. paper code
Addressing catastrophic forgetting in few-shot problems, in ICML, 2021. P. Yap, H. Ritter, and D. Barber. paper code
Few-shot conformal prediction with auxiliary tasks, in ICML, 2021. A. Fisch, T. Schuster, T. Jaakkola, and R. Barzilay. paper code
Few-shot lifelong learning, in AAAI, 2021. P. Mazumder, P. Singh, and P. Rai. paper
Few-shot class-incremental learning via relation knowledge distillation, in AAAI, 2021. S. Dong, X. Hong, X. Tao, X. Chang, X. Wei, and Y. Gong. paper
Few-shot one-class classification via meta-learning, in AAAI, 2021. A. Frikha, D. Krompass, H. Koepken, and V. Tresp. paper code
Practical one-shot federated learning for cross-silo setting, in IJCAI, 2021. Q. Li, B. He, and D. Song. paper code
Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system, in NAACL-HLT, 2021. C. Xia, W. Yin, Y. Feng, and P. S. Yu. paper
Continual few-shot learning for text classification, in EMNLP, 2021. R. Pasunuru, V. Stoyanov, and M. Bansal. paper code
Self-training with few-shot rationalization, in EMNLP, 2021. M. M. Bhat, A. Sordoni, and S. Mukherjee. paper
Diverse distributions of self-supervised tasks for meta-learning in NLP, in EMNLP, 2021. T. Bansal, K. P. Gunasekaran, T. Wang, T. Munkhdalai, and A. McCallum. paper
Generalized and incremental few-shot learning by explicit learning and calibration without forgetting, in ICCV, 2021. A. Kukleva, H. Kuehne, and B. Schiele. paper
Meta learning on a sequence of imbalanced domains with difficulty awareness, in ICCV, 2021. Z. Wang, T. Duan, L. Fang, Q. Suo, and M. Gao. paper code
Synthesized feature based few-shot class-incremental learning on a mixture of subspaces, in ICCV, 2021. A. Cheraghian, S. Rahman, S. Ramasinghe, P. Fang, C. Simon, L. Petersson, and M. Harandi. paper
Few-shot and continual learning with attentive independent mechanisms, in ICCV, 2021. E. Lee, C. Huang, and C. Lee. paper code
Low-shot validation: Active importance sampling for estimating classifier performance on rare categories, in ICCV, 2021. F. Poms, V. Sarukkai, R. T. Mullapudi, N. S. Sohoni, W. R. Mark, D. Ramanan, and K. Fatahalian. paper
Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima, in NeurIPS, 2021. G. SHI, J. CHEN, W. Zhang, L. Zhan, and X. Wu. paper
Variational continual Bayesian meta-learning, in NeurIPS, 2021. Q. Zhang, J. Fang, Z. Meng, S. Liang, and E. Yilmaz. paper
LFPT5: A unified framework for lifelong few-shot language learning based on prompt tuning of T5, in ICLR, 2022. C. Qin, and S. Joty. paper code
Subspace regularizers for few-shot class incremental learning, in ICLR, 2022. A. F. Akyürek, E. Akyürek, D. Wijaya, and J. Andreas. paper code
Meta discovery: Learning to discover novel classes given very limited data, in ICLR, 2022. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, G. Niu, M. Zhou, and M. Sugiyama. paper
Topological transduction for hybrid few-shot learning, in WWW, 2022. J. Chen, and A. Zhang. paper
Continual few-shot relation learning via embedding space regularization and data augmentation, in ACL, 2022. C. Qin, and S. Joty. paper code
Few-shot class-incremental learning for named entity recognition, in ACL, 2022. R. Wang, T. Yu, H. Zhao, S. Kim, S. Mitra, R. Zhang, and R. Henao. paper
Task-adaptive negative envision for few-shot open-set recognition, in CVPR, 2022. S. Huang, J. Ma, G. Han, and S. Chang. paper code
Forward compatible few-shot class-incremental learning, in CVPR, 2022. D. Zhou, F. Wang, H. Ye, L. Ma, S. Pu, and D. Zhan. paper code
Sylph: A hypernetwork framework for incremental few-shot object detection, in CVPR, 2022. L. Yin, J. M. Perez-Rua, and K. J. Liang. paper
Constrained few-shot class-incremental learning, in CVPR, 2022. M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, and A. Rahimi. paper
iFS-RCNN: An incremental few-shot instance segmenter, in CVPR, 2022. K. Nguyen, and S. Todorovic. paper
MetaFSCIL: A meta-learning approach for few-shot class incremental learning, in CVPR, 2022. Z. Chi, L. Gu, H. Liu, Y. Wang, Y. Yu, and J. Tang. paper
Few-shot incremental learning for label-to-image translation, in CVPR, 2022. P. Chen, Y. Zhang, Z. Li, and L. Sun. paper
Revisiting learnable affines for batch norm in few-shot transfer learning, in CVPR, 2022. M. Yazdanpanah, A. A. Rahman, M. Chaudhary, C. Desrosiers, M. Havaei, E. Belilovsky, and S. E. Kahou. paper
Few-shot learning with noisy labels, in CVPR, 2022. K. J. Liang, S. B. Rangrej, V. Petrovic, and T. Hassner. paper
Improving adversarially robust few-shot image classification with generalizable representations, in CVPR, 2022. J. Dong, Y. Wang, J. Lai, and X. Xie. paper
Geometer: Graph few-shot class-incremental learning via prototype representation, in KDD, 2022. B. Lu, X. Gan, L. Yang, W. Zhang, L. Fu, and X. Wang. paper code
Few-shot heterogeneous graph learning via cross-domain knowledge transfer, in KDD, 2022. Q. Zhang, X. Wu, Q. Yang, C. Zhang, and X. Zhang. paper
Few-shot adaptation of pre-trained networks for domain shift, in IJCAI, 2022. W. Zhang, L. Shen, W. Zhang, and C.-S. Foo. paper
MemREIN: Rein the domain shift for cross-domain few-shot learning, in IJCAI, 2022. Y. Xu, L. Wang, Y. Wang, C. Qin, Y. Zhang, and Y. FU. paper
Continual few-shot learning with transformer adaptation and knowledge regularization, in WWW, 2023. X. Wang, Y. Liu, J. Fan, W. Wen, H. Xue, and W. Zhu. paper
DENSE: Data-free one-shot federated learning, in NeurIPS, 2022. J. Zhang, C. Chen, B. Li, L. Lyu, S. Wu, S. Ding, C. Shen, and C. Wu. paper
Towards practical few-shot query sets: Transductive minimum description length inference, in NeurIPS, 2022. S. T. Martin, M. Boudiaf, E. Chouzenoux, J.-C. Pesquet, and I. B. Ayed. paper code
Task-level differentially private meta learning, in NeurIPS, 2022. X. Zhou, and R. Bassily. paper code
FiT: Parameter efficient few-shot transfer learning for personalized and federated image classification, in ICLR, 2023. A. Shysheya, J. F. Bronskill, M. Patacchiola, S. Nowozin, and R. E. Turner. paper code
Towards addressing label skews in one-shot federated learning, in ICLR, 2023. Y. Diao, Q. Li, and B. He. paper code
Data-free one-shot federated learning under very high statistical heterogeneity, in ICLR, 2023. C. E. Heinbaugh, E. Luz-Ricca, and H. Shao. paper code
Contrastive meta-learning for partially observable few-shot learning, in ICLR, 2023. A. Jelley, A. Storkey, A. Antoniou, and S. Devlin. paper code
On the soft-subnetwork for few-shot class incremental learning, in ICLR, 2023. H. Kang, J. Yoon, S. R. H. Madjid, S. J. Hwang, and C. D. Yoo. paper [code](https://github.com/ihaeyong/ SoftNet-FSCIL)
Warping the space: Weight space rotation for class-incremental few-shot learning, in ICLR, 2023. D.-Y. Kim, D.-J. Han, J. Seo, and J. Moon. paper code
Neural collapse inspired feature-classifier alignment for few-shot class-incremental learning, in ICLR, 2023. Y. Yang, H. Yuan, X. Li, Z. Lin, P. Torr, and D. Tao. paper code
Learning with fantasy: Semantic-aware virtual contrastive constraint for few-shot class-incremental learning, in CVPR, 2023. Z. Song, Y. Zhao, Y. Shi, P. Peng, L. Yuan, and Y. Tian. paper code
Few-shot class-incremental learning via class-aware bilateral distillation, in CVPR, 2023. L. Zhao, J. Lu, Y. Xu, Z. Cheng, D. Guo, Y. Niu, and X. Fang. paper code
GKEAL: Gaussian kernel embedded analytic learning for few-shot class incremental task, in CVPR, 2023. H. Zhuang, Z. Weng, R. He, Z. Lin, and Z. Zeng. paper
Glocal energy-based learning for few-shot open-set recognition, in CVPR, 2023. H. Wang, G. Pang, P. Wang, L. Zhang, W. Wei, and Y. Zhang. paper code
Open-set likelihood maximization for few-shot learning, in CVPR, 2023. M. Boudiaf, E. Bennequin, M. Tami, A. Toubhans, P. Piantanida, C. Hudelot, and I. B. Ayed. paper code
Federated few-shot learning, in KDD, 2023. S. Wang, X. Fu, K. Ding, C. Chen, H. Chen, and J. Li. paper code
LFS-GAN: Lifelong few-shot image generation, in ICCV, 2023. J. Seo, J. Kang, and G. Park. paper code
Domain adaptive few-shot open-set learning, in ICCV, 2023. D. Pal, D. More, S. Bhargav, D. Tamboli, V. Aggarwal, and B. Banerjee. paper code
Few-shot continual infomax learning, in ICCV, 2023. Z. Gu, C. Xu, J. Yang, and Z. Cui. paper
Prototypical kernel learning and open-set foreground perception for generalized few-shot semantic segmentation, in ICCV, 2023. K. Huang, F. Wang, Y. Xi, and Y. Gao. paper
DETA: Denoised task adaptation for few-shot learning, in ICCV, 2023. J. Zhang, L. Gao, X. Luo, H. Shen, and J. Song. paper code
Few-shot class-incremental learning via training-free prototype calibration, in NeurIPS, 2023. Q. Wang, D. Zhou, Y. Zhang, D. Zhan, and H. Ye. paper code
Alignment with human representations supports robust few-shot learning, in NeurIPS, 2023. I. Sucholutsky, and T. L. Griffiths. paper
Incremental-DETR: Incremental few-shot object detection via self-supervised learning, in AAAI, 2023. N. Dong, Y. Zhang, M. Ding, and G. H. Lee. paper code
Bayesian cross-modal alignment learning for few-shot out-of-distribution generalization, in AAAI, 2023. L. Zhu, X. Wang, C. Zhou, and N. Ye. paper code
High-level semantic feature matters few-shot unsupervised domain adaptation, in AAAI, 2023. L. Yu, W. Yang, S. Huang, L. Wang, and M. Yang. paper
Enhancing one-shot federated learning through data and ensemble co-boosting, in ICLR, 2024. R. Dai, Y. Zhang, A. Li, T. Liu, X. Yang, and B. Han. paper

Datasets/Benchmarks

FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation, in EMNLP, 2018. X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun. paper code
Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning, arXiv preprint, 2019. T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine. paper code
The Omniglot challenge: A 3-year progress report, in Current Opinion in Behavioral Sciences, 2019. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. paper code
FewRel 2.0: Towards more challenging few-shot relation classification, in EMNLP-IJCNLP, 2019. T. Gao, X. Han, H. Zhu, Z. Liu, P. Li, M. Sun, and J. Zhou. paper code
META-DATASET: A dataset of datasets for learning to learn from few examples, in ICLR, 2020. E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, U. Evci, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P. Manzagol, and H. Larochelle. paper code
Few-shot object detection with attention-rpn and multi-relation detector, in CVPR, 2020. Q. Fan, W. Zhuo, C.-K. Tang, Y.-W. Tai. paper code
FSS-1000: A 1000-class dataset for few-shot segmentation, in CVPR, 2020. X. Li, T. Wei, Y. P. Chen, Y.-W. Tai, and C.-K. Tang. paper code
Impact of base dataset design on few-shot image classification, in ECCV, 2020. O. Sbai, C. Couprie, and M. Aubry. paper code
A unified few-shot classification benchmark to compare transfer and meta learning approaches, in NeurIPS, 2021. V. Dumoulin, N. Houlsby, U. Evci, X. Zhai, R. Goroshin, S. Gelly, and H. Larochelle. paper
Few-shot learning evaluation in natural language understanding, in NeurIPS, 2021. S. Mukherjee, X. Liu, G. Zheng, S. Hosseini, H. Cheng, G. Yang, C. Meek, A. H. Awadallah, and J. Gao. paper code
FS-Mol: A few-shot learning dataset of molecules, in NeurIPS, 2021. M. Stanley, J. Bronskill, K. Maziarz, H. Misztela, J. Lanini, M. H. S. Segler, N. Schneider, and M. Brockschmidt. paper code
RAFT: A real-world few-shot text classification benchmark, in NeurIPS, 2021. N. Alex, E. Lifland, L. Tunstall, A. Thakur, P. Maham, C. J. Riedel, E. Hine, C. Ashurst, P. Sedille, A. Carlier, M. Noetel, and A. Stuhlmüller. paper code
A large-scale benchmark for few-shot program induction and synthesis, in ICML, 2021. F. Alet, J. Lopez-Contreras, J. Koppel, M. Nye, A. Solar-Lezama, T. Lozano-Perez, L. Kaelbling, and J. Tenenbaum. paper code
FEW-NERD: A few-shot named entity recognition dataset, in ACL-IJCNLP, 2021. N. Ding, G. Xu, Y. Chen, X. Wang, X. Han, P. Xie, H. Zheng, and Z. Liu. paper code
CrossFit: A few-shot learning challenge for cross-task generalization in NLP, in EMNLP, 2021. Q. Ye, B. Y. Lin, and X. Ren. paper code
ORBIT: A real-world few-shot dataset for teachable object recognition, in ICCV, 2021. D. Massiceti, L. Zintgraf, J. Bronskill, L. Theodorou, M. T. Harris, E. Cutrell, C. Morrison, K. Hofmann, and S. Stumpf. paper code
FLEX: Unifying evaluation for few-shot NLP, in NeurIPS, 2021. J. Bragg, A. Cohan, K. Lo, and I. Beltagy. paper
Two sides of meta-learning evaluation: In vs. out of distribution, in NeurIPS, 2021. A. Setlur, O. Li, and V. Smith. paper
Realistic evaluation of transductive few-shot learning, in NeurIPS, 2021. O. Veilleux, M. Boudiaf, P. Piantanida, and I. B. Ayed. paper
Meta-Album: Multi-domain meta-dataset for few-shot image classification, in NeurIPS, 2022. I. Ullah, D. Carrión-Ojeda, S. Escalera, I. Guyon, M. Huisman, F. Mohr, J. N. v. Rijn, H. Sun, J. Vanschoren, and P. A. Vu. paper code
Geoclidean: Few-shot generalization in euclidean geometry, in NeurIPS, 2022. J. Hsu, J. Wu, and N. D. Goodman. paper code
FewNLU: Benchmarking state-of-the-art methods for few-shot natural language understanding, in ACL, 2022. Y. Zheng, J. Zhou, Y. Qian, M. Ding, C. Liao, L. Jian, R. Salakhutdinov, J. Tang, S. Ruder, and Z. Yang. paper code
Bongard-HOI: Benchmarking few-shot visual reasoning for human-object interactions, in CVPR, 2022. H. Jiang, X. Ma, W. Nie, Z. Yu, Y. Zhu, and A. Anandkumar. paper code
Hard-Meta-Dataset++: Towards understanding few-shot performance on difficult tasks, in ICLR, 2023. S. Basu, M. Stanley, J. F. Bronskill, S. Feizi, and D. Massiceti. paper
MEWL: Few-shot multimodal word learning with referential uncertainty, in ICML, 2023. G. Jiang, M. Xu, S. Xin, W. Liang, Y. Peng, C. Zhang, and Y. Zhu. paper code
UNISUMM and SUMMZOO: Unified model and diverse benchmark for few-shot summarization, in ACL, 2023. Y. Chen, Y. Liu, R. Xu, Z. Yang, C. Zhu, M. Zeng, and Y. Zhang. paper code
EHRSHOT: An EHR benchmark for few-shot evaluation of foundation models, in NeurIPS, 2023. M. Wornow, R. Thapa, E. Steinberg, J. A. Fries, and N. Shah. paper code
CORE: A few-shot company relation classification dataset for robust domain adaptation, in EMNLP, 2023. P. Borchert, J. D. Weerdt, K. Coussement, A. D. Caigny, and M.-F. Moens. paper code
The COT collection: Improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning, in EMNLP, 2023. S. Kim, S. Joo, D. Kim, J. Jang, S. Ye, J. Shin, and M. Seo. paper code
JASMINE: Arabic GPT models for few-shot learning, in EMNLP, 2023. E. M. B. Nagoudi, M. Abdul-Mageed, A. Elmadany, A. Inciarte, and M. T. I. Khondaker. paper
Fine-tuned LLMs know more, hallucinate less with few-shot sequence-to-sequence semantic parsing over Wikidata, in EMNLP, 2023. S. Xu, S. Liu, T. Culhane, E. Pertseva, M.-H. Wu, S. Semnani, and M. Lam. paper code
MetaCoCo: A new few-shot classification benchmark with spurious correlation, in ICLR, 2024. M. Zhang, H. Li, F. Wu, and K. Kuang. paper
Bongard-OpenWorld: Few-shot reasoning for free-form visual concepts in the real world, in ICLR, 2024. R. Wu, X. Ma, Q. Li, Z. Zhang, W. Wang, S.-C. Zhu, and Y. Wang. paper

Software Library

PaddleFSL, a library for few-shot learning written in PaddlePaddle. link
Torchmeta, a library for few-shot learning & meta-learning written in PyTorch. link
learn2learn, a library for meta-learning written in PyTorch. link
keras-fsl, a library for few-shot learning written in Tensorflow. link

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