What is Point Supervision Worth in Video Instance Segmentation?

• URL: http://arxiv.org/abs/2404.01990v1
• Date: Mon, 1 Apr 2024 17:38:25 GMT
• Title: What is Point Supervision Worth in Video Instance Segmentation?
• Authors: Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan, Subhashree Radhakrishnan, Jose M. Alvarez, Abhinav Shrivastava, Anima Anandkumar,
• Abstract summary: Video instance segmentation (VIS) is a challenging vision task that aims to detect, segment, and track objects in videos. 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. Comprehensive experiments on three VIS benchmarks demonstrate competitive performance of the proposed framework, nearly matching fully supervised methods.
• Score: 119.71921319637748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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