40 citations · 113 across the 31 of their papers we have counts for
5 papers · 1 filter
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
Yuecong Xu, Haozhi Cao, Zhenghua Chen +3
Video analysis tasks such as action recognition have received increasing research interest with growing applications in fields such as smart healthcare, thanks to the introduction…
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
Yuecong Xu, Jianfei Yang, Haozhi Cao +4
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to impr…
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation
Xiyu Wang, Yuecong Xu, Kezhi Mao +1
Assuming the source label space subsumes the target one, Partial Video Domain Adaptation (PVDA) is a more general and practical scenario for cross-domain video classification probl…
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
Yuecong Xu, Jianfei Yang, Haozhi Cao +3
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environme…
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
Yuecong Xu, Jianfei Yang, Haozhi Cao +5
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to cha…