Return of Frustratingly Easy Unsupervised Video Domain Adaptation
Pengfei Wei, Yiqun Sun, Zhiqiang Xu, Yiping Ke, Lawrence Hsieh
Abstract
Unsupervised video domain adaptation (UVDA) is a practical but under-explored problem. In this paper, we propose a frustratingly easy UVDA method, called \emph{MetaTrans}. Specifically, \emph{MetaTrans} adopts a concise learning objective that contains only two fundamental loss terms. Despite the simplicity of the learning objective, \emph{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, \emph{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.
BibTeX
@inproceedings{
wei2026return,
title={Return of Frustratingly Easy Unsupervised Video Domain Adaptation},
author={Pengfei Wei and Yiqun Sun and zhiqiang xu and Yiping Ke and Lawrence B. Hsieh},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=hhhsqmUWsU}
}