Video Instance Segmentation with a Propose-Reduce Paradigm

• URL: http://arxiv.org/abs/2103.13746v1
• Date: Thu, 25 Mar 2021 10:58:36 GMT
• Title: Video Instance Segmentation with a Propose-Reduce Paradigm
• Authors: Huaijia Lin, Ruizheng Wu, Shu Liu, Jiangbo Lu, Jiaya Jia
• Abstract summary: 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 then merge the incomplete results by tracking or matching. We propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step.
• Score: 68.59137660342326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 then 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.

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