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<!DOCTYPE html>
<html lang="en">
<head>
<title>Qian Wang - Homepage</title>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, height=device-height, initial-scale=1.0, viewport-fit=cover" />
<meta name="description" content="Qian Wang's homepage" />
<link rel="icon" href="/media/favicon.ico" />
<style type="text/css">
@import url(https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500&display=swap);
html {
font-family: 'Roboto', sans-serif;
font-weight: 300;
max-width: 100%;
height: 100%;
}
body {
padding: 0;
margin: 0 auto;
max-width: 800px;
min-width: 375px;
}
a {
text-decoration: none;
color: #1772d0;
cursor: pointer;
}
a:hover {
color: #f09228;
}
header {
font-size: 15px;
min-height: 273px;
padding: 20px;
margin: 8px 20px;
padding-right: 220px;
position: relative;
}
header > picture {
position: absolute;
width: 200px;
height: 273px;
right: 0;
top: 0;
bottom: 0;
margin: auto;
}
@media (max-width: 540px) {
header {
padding-right: 0;
}
header > picture {
position: static;
display: block;
margin: auto;
}
}
.self-intro-name {
padding: 14px 0;
text-align: center;
font-weight: 400;
font-size: 32px;
}
.self-intro-links {
text-align: center;
}
h1 {
font-size: 24px;
padding: 20px;
margin: 0;
font-weight: 400;
}
.publication {
padding: 20px;
font-size: 15px;
}
.publication p {
margin: 0;
}
.publication > picture {
float: left;
object-fit: contain;
margin-right: 20px;
overflow: hidden;
}
.publication > picture > img {
transition: transform ease-in-out .3s;
}
.publication > picture > img:hover {
transform: scale(1.1);
}
.publication .title {
font-weight: 500;
margin-bottom: 5px;
}
.publication .authors {
margin-bottom: 5px;
}
.publication .venue {
margin-bottom: 5px;
}
.publication .links {
margin-bottom: 5px;
}
.publication .authors .self {
font-weight: 400;
}
.publication .link::before {
content: "[";
}
.publication .link::after {
content: "]";
}
.publication .link {
margin-right: 3px;
}
.publication .desc {
margin-top: 14px;
font-size: 14px;
}
footer {
text-align: right;
padding: 20px;
font-size: 13px;
}
</style>
</head>
<body>
<header>
<div class="self-intro-name">Qian Wang (王茜)</div>
<picture>
<source srcset="/media/IDPhoto.avif" type="image/avif" />
<img width="200" height="273" src="/media/IDPhoto.JPG" alt="ID Photo" />
</picture>
<div>
<p>
I am a master student at the School of Electronic and Computer Engineering,
<a href="https://www.pku.edu.cn/" target="_blank">Peking University</a>
Shenzhen Graduate School, advised by
<a href="https://jianzhang.tech/" target="_blank">Prof. Jian Zhang</a>.
I received the B.E. degree from the College of Computer Science,
<a href="https://www.scu.edu.cn/" target="_blank">Sichuan University</a>, in 2022.
</p>
<p>
My research interest includes image restoration, image/video generation and image/video editing.
</p>
<div class="self-intro-links">
<a href="mailto:qianwang@stu.pku.edu.cn">Email</a>
|
<a href="https://scholar.google.com/citations?user=YQ4ECikAAAAJ" target="_blank">Google Scholar</a>
|
<a href="https://github.com/akaneqwq/" target="_blank">GitHub</a>
</div>
</div>
</header>
<main>
<h1>Publications</h1>
<div class="publication">
<picture>
<source srcset="/media/Prompt2Poster.avif" type="image/avif" />
<source srcset="/media/Prompt2Poster.webp" type="image/webp" />
<img src="/media/Prompt2Poster.png" width="180" height="140" alt="Prompt2Poster: Automatically Artistic Chinese Poster Creation from Prompt Only" />
</picture>
<div>
<p class="title">Prompt2Poster: Automatically Artistic Chinese Poster Creation from Prompt Only</p>
<div class="authors">
<a href="https://github.com/shaodong233" target="_blank">Shaodong Wang</a>,
<a href="https://github.com/yunyangge" target="_blank">Yunyang Ge</a>,
<a href="https://github.com/LiuhanChen-github" target="_blank">Liuhan Chen</a>,
Haiyang Zhou,
<b>Qian Wang</b>,
<a href="https://cxh0519.github.io/" target="_blank">Xinhua Cheng</a>,
<a href="https://yuanli2333.github.io/" target="_blank">Li Yuan</a></div>
<p class="venue">ACM MM, 2024</p>
<div class="links">
<span class="link">
<a href="https://openreview.net/pdf?id=BQbfGk3JPY" target="_blank">
Paper
</a>
</span>
</div>
<p class="desc">
We propose an automatic poster creation framework, utilizing the capacity of LLM to extract user intention from prompts and generating the aligned background.
</p>
</div>
</div>
<div class="publication">
<picture>
<source srcset="/media/360DVD.avif" type="image/avif" />
<source srcset="/media/360DVD.webp" type="image/webp" />
<img src="/media/360DVD.png" width="180" height="140" alt="360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model" />
</picture>
<div>
<p class="title">360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model</p>
<div class="authors">
<b>Qian Wang</b>,
<a href="https://github.com/lwq20020127/" target="_blank">Weiqi Li</a>,
<a href="https://github.com/MC-E" target="_blank">Chong Mou</a>,
<a href="https://cxh0519.github.io/" target="_blank">Xinhua Cheng</a>,
<a href="https://jianzhang.tech/" target="_blank">Jian Zhang</a></div>
<p class="venue">CVPR, 2024</p>
<div class="links">
<span class="link">
<a href="https://arxiv.org/abs/2401.06578" target="_blank">
Paper
</a>
</span>
<span class="link">
<a href="https://akaneqwq.github.io/360DVD/" target="_blank">
Project
</a>
</span>
<span class="link">
<a href="https://github.com/Akaneqwq/360DVD" target="_blank">
Code
</a>
</span>
</div>
<p class="desc">
We propose a controllable panorama video generation pipeline named 360DVD for generating panoramic videos based on the prompts and motion conditions.
</p>
</div>
</div>
<div class="publication">
<picture>
<source srcset="/media/NTIRE2023.avif" type="image/avif" />
<source srcset="/media/NTIRE2023.webp" type="image/webp" />
<img src="/media/NTIRE2023.png" width="180" height="140" alt="NTIRE 2023 Challenge on 360deg Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results" />
</picture>
<div>
<p class="title">NTIRE 2023 Challenge on 360deg Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results</p>
<div class="authors">
Mingdeng Cao,
et al.,
<b>Qian Wang</b>,
et al.,
Bingchun Luo</div>
<p class="venue">CVPR Workshop, 2023</p>
<div class="links">
<span class="link">
<a href="https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/html/Cao_NTIRE_2023_Challenge_on_360deg_Omnidirectional_Image_and_Video_Super-Resolution_CVPRW_2023_paper.html" target="_blank">
Paper
</a>
</span>
</div>
<p class="desc">
We develop a spatial-temporal two-stage model, wherein the first stage is a 4x image super-resolution network, and the second stage is a 4x video super-resolution network.
</p>
</div>
</div>
<div class="publication">
<picture>
<source srcset="/media/CVPR2023-PCFF.avif" type="image/avif" />
<source srcset="/media/CVPR2023-PCFF.webp" type="image/webp" />
<img src="/media/CVPR2023-PCFF.png" width="180" height="140" alt="Panoptic Compositional Feature Field for Editable Scene Rendering with Network-Inferred Labels via Metric Learning" />
</picture>
<div>
<p class="title">Panoptic Compositional Feature Field for Editable Scene Rendering with Network-Inferred Labels via Metric Learning</p>
<div class="authors">
<a href="https://cxh0519.github.io/" target="_blank">Xinhua Cheng</a>,
<a href="https://github.com/yanmin-wu" target="_blank">Yanmin Wu</a>,
<a href="https://mxjia.github.io/" target="_blank">Mengxi Jia</a>,
<b>Qian Wang</b>,
<a href="https://jianzhang.tech/" target="_blank">Jian Zhang</a></div>
<p class="venue">CVPR, 2023</p>
<div class="links">
<span class="link">
<a href="https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_Panoptic_Compositional_Feature_Field_for_Editable_Scene_Rendering_With_Network-Inferred_CVPR_2023_paper.html" target="_blank">
Paper
</a>
</span>
</div>
<p class="desc">
We introduce metric learing for leveraging 2D network-inferred labels to obtain discriminating feature fields, leading to 3D segmentation and editing results.
</p>
</div>
</div>
<div class="publication">
<picture>
<source srcset="/media/CVPR2022-DGUNet.avif" type="image/avif" />
<source srcset="/media/CVPR2022-DGUNet.webp" type="image/webp" />
<img src="/media/CVPR2022-DGUNet.png" width="180" height="140" alt="Deep Generalized Unfolding Networks for Image Restoration" />
</picture>
<div>
<p class="title">Deep Generalized Unfolding Networks for Image Restoration</p>
<div class="authors">
<a href="https://github.com/MC-E" target="_blank">Chong Mou</a>,
<b>Qian Wang</b>,
<a href="https://jianzhang.tech/" target="_blank">Jian Zhang</a></div>
<p class="venue">CVPR, 2022</p>
<div class="links">
<span class="link">
<a href="https://openaccess.thecvf.com/content/CVPR2022/html/Mou_Deep_Generalized_Unfolding_Networks_for_Image_Restoration_CVPR_2022_paper.html" target="_blank">
Paper
</a>
</span>
<span class="link">
<a href="https://github.com/MC-E/Deep-Generalized-Unfolding-Networks-for-Image-Restoration" target="_blank">
Code
</a>
</span>
</div>
<p class="desc">
We integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent algorithm, driving it to deal with complex real-world image degradation.
</p>
</div>
</div>
<div class="publication">
<picture>
<source srcset="/media/ACMMM2022-MSDPA.avif" type="image/avif" />
<source srcset="/media/ACMMM2022-MSDPA.webp" type="image/webp" />
<img src="/media/ACMMM2022-MSDPA.png" width="180" height="140" alt="More is better: Multi-source Dynamic Parsing Attention for Occluded Person Re-identification" />
</picture>
<div>
<p class="title">More is better: Multi-source Dynamic Parsing Attention for Occluded Person Re-identification</p>
<div class="authors">
<a href="https://cxh0519.github.io/" target="_blank">Xinhua Cheng*</a>,
<a href="https://mxjia.github.io/" target="_blank">Mengxi Jia*</a>,
<b>Qian Wang</b>,
<a href="https://jianzhang.tech/" target="_blank">Jian Zhang</a> (* equal contribution)
</div>
<p class="venue">ACM MM, 2022</p>
<div class="links">
<span class="link">
<a href="https://dl.acm.org/doi/abs/10.1145/3503161.3547819" target="_blank">
Paper
</a>
</span>
</div>
<p class="desc">
We introduce the multi-source knowledge ensemble in occluded re-ID to effective leverage external semantic cues learned from different domains.
</p>
</div>
</div>
<div class="publication">
<picture>
<source srcset="/media/TCSVT2022-VTB.avif" type="image/avif" />
<source srcset="/media/TCSVT2022-VTB.webp" type="image/webp" />
<img src="/media/TCSVT2022-VTB.png" width="180" height="140" alt="A Simple Visual-Textual Baseline for Pedestrian Attribute Recognition" />
</picture>
<div>
<p class="title">A Simple Visual-Textual Baseline for Pedestrian Attribute Recognition</p>
<div class="authors">
<a href="https://cxh0519.github.io/" target="_blank">Xinhua Cheng*</a>,
<a href="https://mxjia.github.io/" target="_blank">Mengxi Jia*</a>,
<b>Qian Wang</b>,
<a href="https://jianzhang.tech/" target="_blank">Jian Zhang</a> (* equal contribution)
</div>
<p class="venue">TCSVT, 2022</p>
<div class="links">
<span class="link">
<a href="https://ieeexplore.ieee.org/document/9588887" target="_blank">
Paper
</a>
</span>
<span class="link">
<a href="https://github.com/cxh0519/VTB" target="_blank">
Code
</a>
</span>
</div>
<p class="desc">
We model pedestrian attribute recognition as a multimodal problem and build a simple visual-textual baseline to captures the intra- and cross-modal correlations.
</p>
</div>
</div>
</main>
<footer>
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