2024 NeurIPS CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition Yuhang Wen, Mengyuan Liu†, Songtao Wu, and Beichen Ding† In Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), 2024 arXiv Bib Code Website @inproceedings{wen2024chase, author = {Wen, Yuhang and Liu, Mengyuan and Wu, Songtao and Ding, Beichen}, title = {CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition}, booktitle = {Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS)}, year = {2024}, } TIP Facial Prior Guided Micro-Expression Generation Yi Zhang, Xinhua Xu, Youjun Zhao, Yuhang Wen, Zixuan Tang, and Mengyuan Liu† IEEE Transactions on Image Processing, 2024 Bib PDF Code @article{zhang2024facial, author = {Zhang, Yi and Xu, Xinhua and Zhao, Youjun and Wen, Yuhang and Tang, Zixuan and Liu, Mengyuan}, journal = {IEEE Transactions on Image Processing}, title = {Facial Prior Guided Micro-Expression Generation}, year = {2024}, volume = {33}, number = {}, pages = {525-540}, doi = {10.1109/TIP.2023.3345177}, dimensions = {true}, } Surfer: Progressive Reasoning with World Models for Robotic Manipulation Pengzhen Ren*, Kaidong Zhang*, Hetao Zheng, Zixuan Li, Yuhang Wen, Fengda Zhu, Mas Ma, and Xiaodan Liang† 2024 arXiv 2023 IROS Interactive Spatiotemporal Token Attention Network for Skeleton-Based General Interactive Action Recognition Yuhang Wen, Zixuan Tang, Yunsheng Pang, Beichen Ding†, and Mengyuan Liu† In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 arXiv Bib PDF Code Website @inproceedings{wen2023interactive, author = {Wen, Yuhang and Tang, Zixuan and Pang, Yunsheng and Ding, Beichen and Liu, Mengyuan}, booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, title = {Interactive Spatiotemporal Token Attention Network for Skeleton-Based General Interactive Action Recognition}, year = {2023}, volume = {}, number = {}, pages = {7886-7892}, doi = {10.1109/IROS55552.2023.10342472}, dimensions = {true}, } 2021 ACM MM Facial Prior Based First Order Motion Model for Micro-Expression Generation Yi Zhang*, Youjun Zhao*, Yuhang Wen, Zixuan Tang, Xinhua Xu, and Mengyuan Liu† In Proceedings of the 29th ACM International Conference on Multimedia, Virtual Event, China, 2021 arXiv Bib PDF Code @inproceedings{fpbfomm2021, author = {Zhang, Yi and Zhao, Youjun and Wen, Yuhang and Tang, Zixuan and Xu, Xinhua and Liu, Mengyuan}, title = {Facial Prior Based First Order Motion Model for Micro-Expression Generation}, year = {2021}, isbn = {9781450386517}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3474085.3479211}, doi = {10.1145/3474085.3479211}, booktitle = {Proceedings of the 29th ACM International Conference on Multimedia}, pages = {4755-4759}, numpages = {5}, keywords = {facial micro-expression, facial landmark, deep learning, micro-expression generation, generative adversarial network}, location = {Virtual Event, China}, series = {MM '21}, dimensions = {true}, } CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images Xinhua Xu*, Yuhang Wen*, Lu Zhao*, Yi Zhang, Youjun Zhao, Zixuan Tang, Ziduo Yang, and Calvin Yu-Chian Chen† Medical Physics, 2021 Bib PDF Code Poster Slides @article{caresunet2021, author = {Xu, Xinhua and Wen, Yuhang and Zhao, Lu and Zhang, Yi and Zhao, Youjun and Tang, Zixuan and Yang, Ziduo and Chen, Calvin Yu-Chian}, title = {CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images}, journal = {Medical Physics}, volume = {48}, number = {11}, pages = {7127-7140}, keywords = {computed tomography (CT) image, content-aware residual UNet, coronavirus disease 2019 (COVID-19), deep learning, segmentation}, doi = {10.1002/mp.15231}, url = {https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.15231}, eprint = {https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15231}, year = {2021}, dimensions = {true}, }