Multi-Task Learning via Co-Attentive Sharing for Pedestrian Attribute Recognition

• URL: http://arxiv.org/abs/2004.03164v1
• Date: Tue, 7 Apr 2020 07:24:22 GMT
• Title: Multi-Task Learning via Co-Attentive Sharing for Pedestrian Attribute Recognition
• Authors: Haitian Zeng, Haizhou Ai, Zijie Zhuang, Long Chen
• Abstract summary: Co-Attentive Sharing (CAS) module extracts discriminative channels and spatial regions for more effective feature sharing in multi-task learning. Our module outperforms the conventional sharing units and achieves superior results compared to the state-of-the-art approaches using many metrics.
• Score: 8.883961218702824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to predict multiple attributes of a pedestrian is a multi-task learning problem. To share feature representation between two individual task networks, conventional methods like Cross-Stitch and Sluice network learn a linear combination of features or feature subspaces. However, linear combination rules out the complex interdependency between channels. Moreover, spatial information exchanging is less-considered. In this paper, we propose a novel Co-Attentive Sharing (CAS) module which extracts discriminative channels and spatial regions for more effective feature sharing in multi-task learning. The module consists of three branches, which leverage different channels for between-task feature fusing, attention generation and task-specific feature enhancing, respectively. Experiments on two pedestrian attribute recognition datasets show that our module outperforms the conventional sharing units and achieves superior results compared to the state-of-the-art approaches using many metrics.

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