Xiaohan Wang

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IEEE Transactions on Multimedia (4)
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ACM Transactions on Multimedia Computing, Communications, and Applications (2)

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MM '21: Proceedings of the 29th ACM International Conference on Multimedia (2)
Computer Vision – ECCV 2024 (1)
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (1)

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Article
VideoAgent: Long-Form Video Understanding with Large Language Model as Agent
- Xiaohan Wang
  https://ror.org/00f54p054Stanford University, Stanford, USA
  ,
- Yuhui Zhang
  https://ror.org/00f54p054Stanford University, Stanford, USA
  ,
- Orr Zohar
  https://ror.org/00f54p054Stanford University, Stanford, USA
  ,
- Serena Yeung-Levy
  https://ror.org/00f54p054Stanford University, Stanford, USA
Computer Vision – ECCV 2024•September 2024, pp 58-76• https://doi.org/10.1007/978-3-031-72989-8_4
Abstract
Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the human cognitive process for long-form video understanding, we ...
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Scalable Video Object Segmentation With Identification Mechanism
- Zongxin Yang
  ReLER, CCAI, Zhejiang University, Hangzhou, China
  ,
- Jiaxu Miao
  School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China
  ,
- Yunchao Wei
  Institute of Information Science, Beijing Jiaotong University, Beijing, China
  ,
- Wenguan Wang
  ReLER, CCAI, Zhejiang University, Hangzhou, China
  ,
- Xiaohan Wang
  ReLER, CCAI, Zhejiang University, Hangzhou, China
  ,
- Yi Yang
  ReLER, CCAI, Zhejiang University, Hangzhou, China
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 46, Issue 9•Sept. 2024, pp 6247-6262 • https://doi.org/10.1109/TPAMI.2024.3383592
This paper delves into the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object Segmentation (VOS). Previous VOS methods decode features with a single positive object, limiting the learning of multi-object ...
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Published By ACM
Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation
- Yucheng Suo
  Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Zhedong Zheng
  University of Macau, Taipa, Macau, China
  ,
- Xiaohan Wang
  Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Bang Zhang
  Alibaba Group, Hangzhou, Zhejiang,China
  ,
- Yi Yang
  Zhejiang University, Hangzhou, Zhejiang, China
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 20, Issue 6•June 2024, Article No.: 185, pp 1-18 • https://doi.org/10.1145/3648368
Sign language provides a way for differently-abled individuals to express their feelings and emotions. However, learning sign language can be challenging and time consuming. An alternative approach is to animate user photos using sign language videos of ...
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SKIM: Skeleton-Based Isolated Sign Language Recognition With Part Mixing
- Kezhou Lin
  Zhejiang University, Hangzhou, China
  ,
- Xiaohan Wang
  Zhejiang University, Hangzhou, China
  ,
- Linchao Zhu
  Zhejiang University, Hangzhou, China
  ,
- Bang Zhang
  DAMO Academy, Alibaba Group, Hangzhou, China
  ,
- Yi Yang
  Zhejiang University, Hangzhou, China
IEEE Transactions on Multimedia, Volume 26•2024, pp 4271-4280 • https://doi.org/10.1109/TMM.2023.3321502
In this article, we present skeleton-based isolated sign language recognition (IsoSLR) with part mixing - SKIM. An IsoSLR model that solely takes the skeleton representation of the human body as input. Previous skeleton-based works either perform worse ...
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Learning to Follow and Generate Instructions for Language-Capable Navigation
- Xiaohan Wang
  Zhejiang University, Hangzhou, China
  ,
- Wenguan Wang
  Zhejiang University, Hangzhou, China
  ,
- Jiayi Shao
  Zhejiang University, Hangzhou, China
  ,
- Yi Yang
  Zhejiang University, Hangzhou, China
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 46, Issue 5•May 2024, pp 3334-3350 • https://doi.org/10.1109/TPAMI.2023.3341828
Visual-language navigation (VLN) is a challenging task that requires embodied agents to follow natural language instructions to navigate in previously unseen environments. However, existing literature put most emphasis on interpreting instructions into ...
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IcoCap: Improving Video Captioning by Compounding Images
- Yuanzhi Liang
  Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia
  ,
- Linchao Zhu
  College of Computer Science and Technology, Zhejiang University, Hangzhou, China
  ,
- Xiaohan Wang
  College of Computer Science and Technology, Zhejiang University, Hangzhou, China
  ,
- Yi Yang
  College of Computer Science and Technology, Zhejiang University, Hangzhou, China
IEEE Transactions on Multimedia, Volume 26•2024, pp 4389-4400 • https://doi.org/10.1109/TMM.2023.3322329
Video captioning is a more challenging task compared to image captioning, primarily due to differences in content density. Video data contains redundant visual content, making it difficult for captioners to generalize diverse content and avoid being ...
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Temporal Pixel-Level Semantic Understanding Through the VSPW Dataset
- Jiaxu Miao
  CCAI, Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Yunchao Wei
  Beijing Jiaotong University, Beijing, China
  ,
- Xiaohan Wang
  CCAI, Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Yi Yang
  CCAI, Zhejiang University, Hangzhou, Zhejiang, China
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 45, Issue 9•Sept. 2023, pp 11297-11308 • https://doi.org/10.1109/TPAMI.2023.3266023
Scene understanding through pixel-level semantic parsing is one of the main problems in computer vision. Till now, image-based methods and datasets for scene parsing have been well explored. However, the real world is naturally dynamic instead of a static ...
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A Differentiable Parallel Sampler for Efficient Video Classification
- Xiaohan Wang
  Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Linchao Zhu
  Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Fei Wu
  Zhejiang University, Hangzhou, Zhejiang, China
  ,
- Yi Yang
  Zhejiang University, Hangzhou, Zhejiang, China
ACM Transactions on Multimedia Computing, Communications, and Applications, Volume 19, Issue 3•May 2023, Article No.: 112, pp 1-18 • https://doi.org/10.1145/3569584
It is crucial to sample a small portion of relevant frames for efficient video classification. The existing methods mainly develop hand-designed sampling strategies or learn sequential selection policies. However, there are two challenges to be solved. ...
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Self-Supervised Point Cloud Representation Learning via Separating Mixed Shapes
- Chao Sun
  School of Computer Science, Zhejiang University, Zhejiang, China
  ,
- Zhedong Zheng
  Sea-NExT joint laboratory, School of Computing, National University of Singapore, Singapore
  ,
- Xiaohan Wang
  School of Computer Science, Zhejiang University, Zhejiang, China
  ,
- Mingliang Xu
  Zhengzhou University, Zhengzhou, Henan, China
  ,
- Yi Yang
  School of Computer Science, Zhejiang University, Zhejiang, China
IEEE Transactions on Multimedia, Volume 25•2023, pp 6207-6218 • https://doi.org/10.1109/TMM.2022.3206664
The manual annotation for large-scale point clouds costs a lot of time and is usually unavailable in harsh real-world scenarios. Inspired by the great success of the pre-training and fine-tuning paradigm in both vision and language tasks, we argue that ...
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Align and Tell: Boosting Text-Video Retrieval With Local Alignment and Fine-Grained Supervision
- Xiaohan Wang
  School of Computer Science, Zhejiang University, Hangzhou, China
  ,
- Linchao Zhu
  School of Computer Science, Zhejiang University, Hangzhou, China
  ,
- Zhedong Zheng
  Sea-NExT Joint Laboratory, School of Computing, National University of Singapore, Singapore
  ,
- Mingliang Xu
  School of Computer Science, Zhengzhou University, Zhengzhou, China
  ,
- Yi Yang
  School of Computer Science, Zhejiang University, Hangzhou, China
IEEE Transactions on Multimedia, Volume 25•2023, pp 6079-6089 • https://doi.org/10.1109/TMM.2022.3204444
Text-video retrieval is one of the basic tasks for multimodal research and has been widely harnessed in many real-world systems. Most existing approaches directly compare the global representation between videos and text descriptions and utilize the ...
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CenterCLIP: Token Clustering for Efficient Text-Video Retrieval
- Shuai Zhao
  Zhejiang University, Hangzhou, China
  ,
- Linchao Zhu
  University of Technology Sydney, Sydney, NSW, Australia
  ,
- Xiaohan Wang
  Zhejiang University, Hangzhou, China
  ,
- Yi Yang
  Zhejiang University, Hangzhou, China
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2022, pp 970-981• https://doi.org/10.1145/3477495.3531950

Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision transformer of CLIP, ...
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Published By ACM
Published By ACM
WAB'21: 1st Workshop on Multimodal Product Identification in Livestreaming and WAB Challenge
- Yueting Zhuang
  Zhejiang University, Hangzhou, China
  ,
- Xing Tang
  Alibaba Group, Hangzhou, China
  ,
- Guilin Wu
  Alibaba Group, Hangzhou, China
  ,
- Yahong Han
  Tianjin University, Tianjin, China
  ,
- Haihong Tang
  Alibaba Group, Hangzhou, China
  ,
- Xiaobo Li
  Alibaba Group, Hangzhou, China
  ,
- Xiaohan Wang
  Zhejiang University, Hangzhou, China
  ,
- Baoming Yan
  Alibaba Group, Hangzhou, China
  ,
- Bo Gao
  Alibaba Group, Hangzhou, China
  ,
- Yi Yang
  University of Technology Sydney, Sydney, Australia
MM '21: Proceedings of the 29th ACM International Conference on Multimedia•October 2021, pp 5710-5711• https://doi.org/10.1145/3474085.3478584

Product identification has become a very important component in the modern E-commerce shopping system. Consumers could enjoy watching livingstreaming and buying products that livestream hosts recommended. However, with hundreds of products presented in ...
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Published By ACM
Video-to-Image Casting: A Flatting Method for Video Analysis
- Xu Chen
  Chongqing University of Posts and Telecommunications & Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China
  ,
- Chenqiang Gao
  Chongqing University of Posts and Telecommunications & Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China
  ,
- Feng Yang
  Chongqing University of Posts and Telecommunications & Chongqing Key Laboratory of Signal and Information Processing, Chongqing, China
  ,
- Xiaohan Wang
  Zhejiang University & Baidu Research, Hangzhou, China
  ,
- Yi Yang
  Zhejiang University, Hangzhou, China
  ,
- Yahong Han
  Tianjin University & Peng Cheng Laboratory, Tianjin, China
MM '21: Proceedings of the 29th ACM International Conference on Multimedia•October 2021, pp 4958-4966• https://doi.org/10.1145/3474085.3475424

Previous mainstream video analysis methods, especially 3D CNNs-based models, mainly aim to transfer frameworks from the image domain to the video domain, and they follow the regime which has been succeeded in image processing, i.e., large-scale ...
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ACM Author-Izer Service

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ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

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Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

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Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

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Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

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Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

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Xiaohan Wang

Xiaohan Wang

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VideoAgent: Long-Form Video Understanding with Large Language Model as Agent

Scalable Video Object Segmentation With Identification Mechanism

Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation

SKIM: Skeleton-Based Isolated Sign Language Recognition With Part Mixing

Learning to Follow and Generate Instructions for Language-Capable Navigation

IcoCap: Improving Video Captioning by Compounding Images

Temporal Pixel-Level Semantic Understanding Through the VSPW Dataset

A Differentiable Parallel Sampler for Efficient Video Classification

Self-Supervised Point Cloud Representation Learning via Separating Mixed Shapes

Align and Tell: Boosting Text-Video Retrieval With Local Alignment and Fine-Grained Supervision

CenterCLIP: Token Clustering for Efficient Text-Video Retrieval

WAB'21: 1st Workshop on Multimodal Product Identification in Livestreaming and WAB Challenge

Video-to-Image Casting: A Flatting Method for Video Analysis