This paper proposes a simple self-supervised approach for learning a
representation for visual correspondence from raw video. We cast correspondence
as prediction of links in a space-time graph constructed from video. In this
graph, the nodes are patches sampled from each frame, and nodes adjacent in
time can share a directed edge. We learn a representation in which pairwise
similarity defines transition probability of a random walk, so that long-range
correspondence is computed as a walk along the graph. We optimize the
representation to place high probability along paths of similarity. Targets for
learning are formed without supervision, by cycle-consistency: the objective is
to maximize the likelihood of returning to the initial node when walking along
a graph constructed from a palindrome of frames. Thus, a single path-level
constraint implicitly supervises chains of intermediate comparisons. When used
as a similarity metric without adaptation, the learned representation
outperforms the self-supervised state-of-the-art on label propagation tasks
involving objects, semantic parts, and pose. Moreover, we demonstrate that a
technique we call edge dropout, as well as self-supervised adaptation at
test-time, further improve transfer for object-centric correspondence.

该论文提出了一种简单的自监督方法来学习从原始视频中获取图像匹配的表达方式，通过构建空间时间图来预测连接预测匹配，并通过回文构造的图优化表示方法。