Current top-leading solutions for video object segmentation (VOS) typically
follow a matching-based regime: for each query frame, the segmentation mask is
inferred according to its correspondence to previously processed and the first
annotated frames. They simply exploit the supervisory signals from the
groundtruth masks for learning mask prediction only, without posing any
constraint on the space-time correspondence matching, which, however, is the
fundamental building block of such regime. To alleviate this crucial yet
commonly ignored issue, we devise a correspondence-aware training framework,
which boosts matching-based VOS solutions by explicitly encouraging robust
correspondence matching during network learning. Through comprehensively
exploring the intrinsic coherence in videos on pixel and object levels, our
algorithm reinforces the standard, fully supervised training of mask
segmentation with label-free, contrastive correspondence learning. Without
neither requiring extra annotation cost during training, nor causing speed
delay during deployment, nor incurring architectural modification, our
algorithm provides solid performance gains on four widely used benchmarks,
i.e., DAVIS2016&2017, and YouTube-VOS2018&2019, on the top of famous
matching-based VOS solutions.

本研究提出了一种基于对空间 - 时间对应匹配的显式监督学习的视频物体分割方法，该方法可以在不增加额外训练成本、没有速度延迟和不需要架构修改的情况下提高目前最先进的匹配式 VOS 方法的性能。