Learning Video Object Segmentation from Unlabeled Videos

• URL: http://arxiv.org/abs/2003.05020v1
• Date: Tue, 10 Mar 2020 22:12:15 GMT
• Title: Learning Video Object Segmentation from Unlabeled Videos
• Authors: Xiankai Lu, Wenguan Wang, Jianbing Shen, Yu-Wing Tai, David Crandall, and Steven C. H. Hoi
• Abstract summary: We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos. We introduce a unified unsupervised/weakly supervised learning framework, called MuG, that comprehensively captures properties of VOS at multiple granularities.
• Score: 158.18207922363783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensive annotated data. We introduce a unified unsupervised/weakly supervised learning framework, called MuG, that comprehensively captures intrinsic properties of VOS at multiple granularities. Our approach can help advance understanding of visual patterns in VOS and significantly reduce annotation burden. With a carefully-designed architecture and strong representation learning ability, our learned model can be applied to diverse VOS settings, including object-level zero-shot VOS, instance-level zero-shot VOS, and one-shot VOS. Experiments demonstrate promising performance in these settings, as well as the potential of MuG in leveraging unlabeled data to further improve the segmentation accuracy.

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