Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete results by tracking or matching. These methods may cause error accumulation in the merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step. We further build a sequence propagation head on the existing image-level instance segmentation network for long-term propagation. To ensure robustness and high recall of our proposed framework, multiple sequences are proposed where redundant sequences of the same instance are reduced. We achieve state-of-the-art performance on two representative benchmark datasets -- we obtain 47.6% in terms of AP on YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code is available at https://github.com/dvlab-research/ProposeReduce.
@article{arxiv.2103.13746,
title = {Video Instance Segmentation with a Propose-Reduce Paradigm},
author = {Huaijia Lin and Ruizheng Wu and Shu Liu and Jiangbo Lu and Jiaya Jia},
journal= {arXiv preprint arXiv:2103.13746},
year = {2021}
}