Unsupervised Co-part Segmentation through Assembly
This addresses the problem of segmenting object parts without labeled data for applications in computer vision, representing a novel method rather than an incremental improvement.
The paper tackles unsupervised co-part segmentation from images by leveraging motion in videos and introducing a part-assembly procedure for self-supervision, achieving meaningful and compact segmentation that outperforms state-of-the-art methods on diverse benchmarks.
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.