What is Point Supervision Worth in Video Instance Segmentation?

Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan, Subhashree Radhakrishnan, Jose M. Alvarez, Abhinav Shrivastava, Anima Anandkumar; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 2671-2681

Video instance segmentation (VIS) is a challenging vision task that aims to detect segment and track objects in videos. Conventional VIS methods rely on densely annotated object masks which are expensive. We reduce the human annotations to only one point for each object in a video frame during training and obtain high-quality mask predictions close to fully supervised models. Our proposed training method consists of a class-agnostic proposal generation module to provide rich negative samples and a spatio-temporal point-based matcher to match the object queries with the provided point annotations. Comprehensive experiments on three VIS benchmarks demonstrate competitive performance of the proposed framework nearly matching fully supervised methods.