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.

本研究针对动作识别中无监督视频域适应的挑战性问题，特别关注具有相当大的域差异而不是现有工作主要处理标记源领域和未标记目标领域之间的小域差异的情况。为了建立更真实的环境，我们引入了一种新的UVDA场景，称为Kinetics->BABEL，该场景在时间动态和背景偏移方面具有更大的域差异。为了解决源领域和目标领域之间的动作持续时间差异所带来的时间差异问题，我们提出了一种全局局部视图对齐方法。为了减轻背景偏移问题，我们通过时间顺序学习来学习时间顺序敏感的表示，并通过背景增强来学习背景不变的表示。我们通过实验证明，所提出的方法在具有大域差异的Kinetics->BABEL数据集上相比现有方法表现出显著的改进。代码可以在此https URL找到。

GLAD：大领域差异下无监督视频领域自适应中的全局局部视图对齐和背景去偏