Improving Unsupervised Label Propagation for Pose Tracking and Video Object Segmentation

Abstract

Label propagation is a challenging task in computer vision with many applications. One approach is to learn representations of visual correspondence. In this paper, we study recent works on label propagation based on correspondence, carefully evaluate the effect of various aspects of their implementation, and improve upon various details. Our pipeline assembled from these best practices outperforms the previous state of the art in terms of PCK0.1 on the JHMDB dataset by 6.5%. We also propose a novel joint framework for tracking and keypoint propagation, which in contrast to the core pipeline is applicable to tracking small objects and obtains results that substantially exceed the performance of the core pipeline. Finally, for VOS, we extend the core pipeline to a fully unsupervised one by initializing the first frame with the self-attention layer from DINO. Our pipeline for VOS runs online and can handle static objects. It outperforms unsupervised frameworks with these characteristics.