CASIA OpenIR  > 多模态人工智能系统全国重点实验室
HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network
Yu C(于畅)1,2; Zhu XY(朱翔昱)1,2; Zhang XM(张小梅)1,2; Wang ZD(王子都)1,2; Lei Z(雷震)1,2,3; Zhang ZX(张兆翔)1,2,3
2022
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2022年6月
Conference Place美国
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.

Sub direction classification生物特征识别
planning direction of the national heavy laboratory可解释人工智能
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56726
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorZhang ZX(张兆翔)
Affiliation1.中国科学院自动化所
2.中国科学院大学
3.Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yu C,Zhu XY,Zhang XM,et al. HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network[C],2022.
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