  Video analysis tasks rely heavily on identifying the pixels from different
frames that correspond to the same visual target. To tackle this problem,
recent studies have advocated feature learning methods that aim to learn
distinctive representations to match the pixels, especially in a
self-supervised fashion. Unfortunately, these methods have difficulties for
tiny or even single-pixel visual targets. Pixel-wise video correspondences were
traditionally related to optical flows, which however lead to deterministic
correspondences and lack robustness on real-world videos. We address the
problem of learning features for establishing pixel-wise correspondences.
Motivated by optical flows as well as the self-supervised feature learning, we
propose to use not only labeled synthetic videos but also unlabeled real-world
videos for learning fine-grained representations in a holistic framework. We
adopt an adversarial learning scheme to enhance the generalization ability of
the learned features. Moreover, we design a coarse-to-fine framework to pursue
high computational efficiency. Our experimental results on a series of
correspondence-based tasks demonstrate that the proposed method outperforms
state-of-the-art rivals in both accuracy and efficiency.
