Improving Facial Attribute Recognition by Group and Graph Learning

• URL: http://arxiv.org/abs/2105.13825v1
• Date: Fri, 28 May 2021 13:36:28 GMT
• Title: Improving Facial Attribute Recognition by Group and Graph Learning
• Authors: Zhenghao Chen and Shuhang Gu and Feng Zhu and Jing Xu and Rui Zhao
• Abstract summary: Exploiting the relationships between attributes is a key challenge for improving facial attribute recognition. In this work, we are concerned with two types of correlations that are spatial and non-spatial relationships. We propose a unified network called Multi-scale Group and Graph Network.
• Score: 34.39507051712628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Exploiting the relationships between attributes is a key challenge for improving multiple facial attribute recognition. In this work, we are concerned with two types of correlations that are spatial and non-spatial relationships. For the spatial correlation, we aggregate attributes with spatial similarity into a part-based group and then introduce a Group Attention Learning to generate the group attention and the part-based group feature. On the other hand, to discover the non-spatial relationship, we model a group-based Graph Correlation Learning to explore affinities of predefined part-based groups. We utilize such affinity information to control the communication between all groups and then refine the learned group features. Overall, we propose a unified network called Multi-scale Group and Graph Network. It incorporates these two newly proposed learning strategies and produces coarse-to-fine graph-based group features for improving facial attribute recognition. Comprehensive experiments demonstrate that our approach outperforms the state-of-the-art methods.

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