Contrastive learning has recently narrowed the gap between self-supervised
and supervised methods in image and video domain. State-of-the-art video
contrastive learning methods such as CVRL and $\rho$-MoCo spatiotemporally
augment two clips from the same video as positives. By only sampling positive
clips locally from a single video, these methods neglect other semantically
related videos that can also be useful. To address this limitation, we leverage
nearest-neighbor videos from the global space as additional positive pairs,
thus improving positive key diversity and introducing a more relaxed notion of
similarity that extends beyond video and even class boundaries. Our method,
Inter-Intra Video Contrastive Learning (IIVCL), improves performance on a range
of video tasks.

该研究介绍了一种叫做 Inter-Intra Video Contrastive Learning (IIVCL) 的方法，利用来自全局空间的最近邻视频作为额外的正对。该方法提高了正关键图的多样性，并引入了一个更松散的相似性概念，扩展到了视频甚至跨类边界。在一系列视频任务上，性能得到了提高。