In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics->BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics->BABEL dataset with a large domain gap. The code is available at https://github.com/KHUVLL/GLAD.

翻译：本文旨在解决无监督视频域适应（UVDA）在动作识别中的挑战性问题。我们特别关注具有显著域间隙的场景，这与现有工作主要处理标注源域与未标注目标域之间的小域间隙形成对比。为建立更真实的设定，我们引入了一种新颖的UVDA场景——Kinetics->BABEL，其在时间动态和背景偏移方面均具有更大的域间隙。为应对时间偏移（即源域与目标域之间的动作持续时间差异），我们提出了一种全局-局部视角对齐方法。为缓解背景偏移，我们提出通过时间顺序学习来获得时间顺序敏感的表征，并通过背景增强学习背景不变的表征。实验证明，所提方法在具有大域间隙的Kinetics->BABEL数据集上较现有方法有显著提升。代码已开源：https://github.com/KHUVLL/GLAD。