HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network

• URL: http://arxiv.org/abs/2203.10699v1
• Date: Mon, 21 Mar 2022 01:39:41 GMT
• Title: HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network
• Authors: Chang Yu, Xiangyu Zhu, Xiaomei Zhang, Zidu Wang, Zhaoxiang Zhang, Zhen Lei
• Abstract summary: We propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised face subpart-part discovery. HP-Capsule extends the application of capsule networks from digits to human faces and takes a step forward to show how the neural networks understand objects without human intervention.
• Score: 76.92310948325847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capsule networks are designed to present the objects by a set of parts and their relationships, which provide an insight into the procedure of visual perception. Although recent works have shown the success of capsule networks on simple objects like digits, the human faces with homologous structures, which are suitable for capsules to describe, have not been explored. In this paper, we propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised face subpart-part discovery. When browsing large-scale face images without labels, the network first encodes the frequently observed patterns with a set of explainable subpart capsules. Then, the subpart capsules are assembled into part-level capsules through a Transformer-based Parsing Module (TPM) to learn the compositional relations between them. During training, as the face hierarchy is progressively built and refined, the part capsules adaptively encode the face parts with semantic consistency. HP-Capsule extends the application of capsule networks from digits to human faces and takes a step forward to show how the neural networks understand homologous objects without human intervention. Besides, HP-Capsule gives unsupervised face segmentation results by the covered regions of part capsules, enabling qualitative and quantitative evaluation. Experiments on BP4D and Multi-PIE datasets show the effectiveness of our method.

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