  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).
