Unsupervised Co-part Segmentation through Assembly

• URL: http://arxiv.org/abs/2106.05897v1
• Date: Thu, 10 Jun 2021 16:22:53 GMT
• Title: Unsupervised Co-part Segmentation through Assembly
• Authors: Qingzhe Gao, Bin Wang, Libin Liu, Baoquan Chen
• Abstract summary: We propose an unsupervised learning approach for co-part segmentation from images. We leverage motion information embedded in videos and explicitly extract latent representations to segment meaningful object parts. We show that our approach can achieve meaningful and compact part segmentation, outperforming state-of-the-art approaches on diverse benchmarks.
• Score: 42.874278526843305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For the training stage, we leverage motion information embedded in videos and explicitly extract latent representations to segment meaningful object parts. More importantly, we introduce a dual procedure of part-assembly to form a closed loop with part-segmentation, enabling an effective self-supervision. We demonstrate the effectiveness of our approach with a host of extensive experiments, ranging from human bodies, hands, quadruped, and robot arms. We show that our approach can achieve meaningful and compact part segmentation, outperforming state-of-the-art approaches on diverse benchmarks.

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