In this paper, we focus on the self-supervised learning of visual
correspondence using unlabeled videos in the wild. Our method simultaneously
considers intra- and inter-video representation associations for reliable
correspondence estimation. The intra-video learning transforms the image
contents across frames within a single video via the frame pair-wise affinity.
To obtain the discriminative representation for instance-level separation, we
go beyond the intra-video analysis and construct the inter-video affinity to
facilitate the contrastive transformation across different videos. By forcing
the transformation consistency between intra- and inter-video levels, the
fine-grained correspondence associations are well preserved and the
instance-level feature discrimination is effectively reinforced. Our simple
framework outperforms the recent self-supervised correspondence methods on a
range of visual tasks including video object tracking (VOT), video object
segmentation (VOS), pose keypoint tracking, etc. It is worth mentioning that
our method also surpasses the fully-supervised affinity representation (e.g.,
ResNet) and performs competitively against the recent fully-supervised
algorithms designed for the specific tasks (e.g., VOT and VOS).

本研究旨在通过使用野外未标记视频进行自监督学习的视觉对应关系。我们的方法同时考虑了可靠对应估计的视频内部和视频间的表示关联。