Reducing Annotation Cost for Video Instance Segmentation with Point Supervision

This work introduces a point-supervised video instance segmentation framework that can achieve competitive performance compared to fully-supervised methods, by leveraging class-agnostic proposal generation and a spatio-temporal point-based matcher to generate high-quality dense pseudo-labels from sparse point annotations.