AI RESEARCH
Learnable Motion-Focused Tokenization for Effective and Efficient Video Unsupervised Domain Adaptation
arXiv CS.CV
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ArXi:2604.09955v1 Announce Type: new Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled source domain to an unlabeled target domain. Despite recent advances, existing VUDA methods often fall short of fully supervised performance, a key reason being the prevalence of static and uninformative backgrounds that exacerbate domain shifts. Additionally, prior approaches largely overlook computational efficiency, limiting real-world adoption.