Video domain generalization aims to learn generalizable video classification models for unseen target domains by training in a source domain. A critical challenge of video domain generalization is to defend against the heavy reliance on domain-specific cues extracted from the source domain when recognizing target videos. To this end, we propose to perceive diverse spatial-temporal cues in videos, aiming to discover potential domain-invariant cues in addition to domain-specific cues. We contribute a novel model named Spatial-Temporal Diversification Network (STDN), which improves the diversity from both space and time dimensions of video data. First, our STDN proposes to discover various types of spatial cues within individual frames by spatial grouping. Then, our STDN proposes to explicitly model spatial-temporal dependencies between video contents at multiple space-time scales by spatial-temporal relation modeling. Extensive experiments on three benchmarks of different types demonstrate the effectiveness and versatility of our approach.

翻译：视频领域泛化旨在通过在源域中训练，学习能够泛化到未见目标域的视频分类模型。视频领域泛化的一个关键挑战是，在识别目标视频时，需防范模型过度依赖从源域提取的领域特定线索。为此，我们提出感知视频中多样化的时空线索，旨在除领域特定线索外，发现潜在的领域不变线索。我们贡献了一个名为时空多样化网络（Spatial-Temporal Diversification Network, STDN）的新模型，该模型从视频数据的空间与时间两个维度提升多样性。首先，我们的STDN通过空间分组方法，在单个帧内发现多样化的空间线索。随后，通过时空关系建模，显式建模视频内容在多个时空尺度上的时空依赖关系。在三种不同类型基准上的大量实验表明，我们的方法具有有效性与通用性。