Return of Frustratingly Easy Unsupervised Video Domain Adaptation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909056171507712 |
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| author | Wei, Pengfei Sun, Yiqun Xu, Zhiqiang Ke, Yiping Hsieh, Lawrence B. |
| author_facet | Wei, Pengfei Sun, Yiqun Xu, Zhiqiang Ke, Yiping Hsieh, Lawrence B. |
| contents | Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, called MetaTrans. Specifically, MetaTrans adopts a concise learning objective that contains only two fundamental loss terms. Despite the simplicity of the learning objective, MetaTrans embodies an advanced UVDA idea, that is, handling the spatial and temporal divergence of cross-domain videos separately, through a subtle model architecture design. By implementing a temporal-static subtraction module, MetaTrans effectively removes spatial and temporal divergence. Extensive empirical evaluations, particularly on various cross-domain action recognition tasks, show substantial absolute adaptation performance enhancement and significantly superior relative performance gain compared with state-of-the-art UVDA baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19510 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Return of Frustratingly Easy Unsupervised Video Domain Adaptation Wei, Pengfei Sun, Yiqun Xu, Zhiqiang Ke, Yiping Hsieh, Lawrence B. Computer Vision and Pattern Recognition Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, called MetaTrans. Specifically, MetaTrans adopts a concise learning objective that contains only two fundamental loss terms. Despite the simplicity of the learning objective, MetaTrans embodies an advanced UVDA idea, that is, handling the spatial and temporal divergence of cross-domain videos separately, through a subtle model architecture design. By implementing a temporal-static subtraction module, MetaTrans effectively removes spatial and temporal divergence. Extensive empirical evaluations, particularly on various cross-domain action recognition tasks, show substantial absolute adaptation performance enhancement and significantly superior relative performance gain compared with state-of-the-art UVDA baselines. |
| title | Return of Frustratingly Easy Unsupervised Video Domain Adaptation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.19510 |