Recent works have advanced the performance of self-supervised representation
learning by a large margin. The core among these methods is intra-image
invariance learning. Two different transformations of one image instance are
considered as a positive sample pair, where various tasks are designed to learn
invariant representations by comparing the pair. Analogically, for video data,
representations of frames from the same video are trained to be closer than
frames from other videos, i.e. intra-video invariance. However, cross-video
relation has barely been explored for visual representation learning. Unlike
intra-video invariance, ground-truth labels of cross-video relation is usually
unavailable without human labors. In this paper, we propose a novel contrastive
learning method which explores the cross-video relation by using
cycle-consistency for general image representation learning. This allows to
collect positive sample pairs across different video instances, which we
hypothesize will lead to higher-level semantics. We validate our method by
transferring our image representation to multiple downstream tasks including
visual object tracking, image classification, and action recognition. We show
significant improvement over state-of-the-art contrastive learning methods.
Project page is available at this https URL

本文研究了自监督表示学习的核心方法之一 —— 图像内部不变性学习，提出了一种新的基于循环一致性的对比学习方法，探索了跨视频关系，实现了图像表示的有效学习，并在多个下游任务中获得了显著的改进。