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Reducing Vision-Answer Biases for Multiple-Choice VQA 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 4621-4634
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(2684Kb)  |  收藏  |  浏览/下载：84/2  |  提交时间：2023/11/17
Multiple-choice VQA  vision-answer bias  causal intervention  counterfactual interaction learning  
Explicit Cross-Modal Representation Learning for Visual Commonsense Reasoning 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 2986-2997
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(5681Kb)  |  收藏  |  浏览/下载：401/1  |  提交时间：2022/07/25
Cognition  Video recording  Syntactics  Visualization  Task analysis  Semantics  Linguistics  Visual Commonsense Reasoning  explicit reasoning  syntactic structure  interpretability  
Holographic Feature Learning of Egocentric-Exocentric Videos for Multi-Domain Action Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 2273-2286
作者:  Huang, Yi;  Yang, Xiaoshan;  Gao, Junyun;  Xu, Changsheng
Adobe PDF(2409Kb)  |  收藏  |  浏览/下载：368/74  |  提交时间：2022/07/25
Videos  Feature extraction  Visualization  Task analysis  Computational modeling  Target recognition  Prototypes  Egocentric videos  exocentric videos  holographic feature  multi-domain  action recognition  
GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation 会议论文
IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, JUN 16-20, 2019
作者:  Ma, Xinhong;  Zhang, Tianzhu;  Xu, Changsheng
Adobe PDF(7239Kb)  |  收藏  |  浏览/下载：223/46  |  提交时间：2022/06/14
Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 1800-1814
作者:  Zhang, Feifei;  Xu, Mingliang;  Xu, Changsheng
收藏  |  浏览/下载：264/0  |  提交时间：2022/06/10
Noise measurement  Face recognition  Data models  Task analysis  Training data  Training  Annotations  Facial expression recognition  noisy labeled data  clean labels  end-to-end  pose modeling  noise modeling  
Margin-Based Adversarial Joint Alignment Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 4, 页码: 2057-2067
作者:  Zuo, Yukun;  Yao, Hantao;  Zhuang, Liansheng;  Xu, Changsheng
收藏  |  浏览/下载：295/0  |  提交时间：2022/06/10
Feature extraction  Adaptation models  Image reconstruction  Generative adversarial networks  Semisupervised learning  Data models  Training  Domain adaptation  joint alignment module  margin-based generative module  
Towards Corruption-Agnostic Robust Domain Adaptation 期刊论文
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2022, 卷号: 18, 期号: 4, 页码: 16
作者:  Xu, Yifan;  Sheng, Kekai;  Dong, Weiming;  Wu, Baoyuan;  Xu, Changsheng;  Hu, Bao-Gang
Adobe PDF(2116Kb)  |  收藏  |  浏览/下载：459/99  |  提交时间：2022/06/10
Domain adaptation  corruption robustness  transfer learning  
Joint Expression Synthesis and Representation Learning for Facial Expression Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 3, 页码: 1681-1695
作者:  Zhang, Xi;  Zhang, Feifei;  Xu, Changsheng
Adobe PDF(4827Kb)  |  收藏  |  浏览/下载：261/1  |  提交时间：2022/06/06
Face recognition  Task analysis  Generative adversarial networks  Image synthesis  Image recognition  Faces  Training  Facial expression recognition  facial image synthesis  generative adversarial network  representation learning  
A unified framework for multi-modal federated learning 期刊论文
NEUROCOMPUTING, 2022, 卷号: 480, 页码: 110-118
作者:  Xiong, Baochen;  Yang, Xiaoshan;  Qi, Fan;  Xu, Changsheng
收藏  |  浏览/下载：276/0  |  提交时间：2022/06/06
Multi-modal  Federated learning  Co-attention  
Seek Common Ground While Reserving Differences: A Model-Agnostic Module for Noisy Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 1020-1030
作者:  Zuo, Yukun;  Yao, Hantao;  Zhuang, Liansheng;  Xu, Changsheng
收藏  |  浏览/下载：236/0  |  提交时间：2022/06/06
Noise measurement  Adaptation models  Predictive models  Reliability  Task analysis  Standards  Data models  Noisy domain adaptation  Seek common ground component  Reserve differences component