'Boundary-Aware Segmentation Network for Mobile and Web Applications', Xuebin Qin, Deng-Ping Fan, Chenyang Huang, Cyril Diagne, Zichen Zhang, Adria Cabeza Sant’Anna, Albert Suarez, Martin Jagersand, and Ling Shao.
SOD Results will come soon!
SOC Results will come soon!
COD Results
Code for CVPR 2019 paper 'BASNet: Boundary-Aware Salient Object Detection code', Xuebin Qin, Zichen Zhang, Chenyang Huang, Chao Gao, Masood Dehghan and Martin Jagersand.
Contact: xuebin[at]ualberta[dot]ca
(2020-May-09) NEWS! Our new Salient Object Detection model (U^2-Net), which is just accepted by Pattern Recognition, is available now!
U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Python 3.6
numpy 1.15.2
scikit-image 0.14.0
PIL 5.2.0
PyTorch 0.4.0
torchvision 0.2.1
glob
The SSIM loss is adapted from pytorch-ssim.
- Clone this repo
git clone https://github.com/NathanUA/BASNet.git
-
Download the pre-trained model basnet.pth from GoogleDrive or baidu extraction code: 6phq, and put it into the dirctory 'saved_models/basnet_bsi/'
-
Cd to the directory 'BASNet', run the training or inference process by command:
python basnet_train.pyorpython basnet_test.pyrespectively.
We also provide the predicted saliency maps (GoogleDrive,Baidu) for datasets SOD, ECSSD, DUT-OMRON, PASCAL-S, HKU-IS and DUTS-TE.
@article{DBLP:journals/corr/abs-2101-04704,
author = {Xuebin Qin and
Deng{-}Ping Fan and
Chenyang Huang and
Cyril Diagne and
Zichen Zhang and
Adri{\`{a}} Cabeza Sant'Anna and
Albert Su{\`{a}}rez and
Martin J{\"{a}}gersand and
Ling Shao},
title = {Boundary-Aware Segmentation Network for Mobile and Web Applications},
journal = {CoRR},
volume = {abs/2101.04704},
year = {2021},
url = {https://arxiv.org/abs/2101.04704},
archivePrefix = {arXiv},
}
@InProceedings{Qin_2019_CVPR,
author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Gao, Chao and Dehghan, Masood and Jagersand, Martin},
title = {BASNet: Boundary-Aware Salient Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
