What is Point Supervision Worth in Video Instance Segmentation?
<|reference_start|>What is Point Supervision Worth in Video Instance Segmentation?: 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.<|reference_end|>
@article{huang2024what,
    title={What is Point Supervision Worth in Video Instance Segmentation?},
    author={Shuaiyi Huang, De-An Huang, Zhiding Yu, Shiyi Lan, Subhashree
  Radhakrishnan, Jose M. Alvarez, Abhinav Shrivastava, Anima Anandkumar},
    journal={arXiv preprint arXiv:2404.01990},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01990},
    primaryClass={cs.CV}
}