Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2304.04205v1
• Date: Sun, 9 Apr 2023 10:22:10 GMT
• Title: Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification
• Authors: Jiawei Feng and Ancong Wu and Wei-Shi Zheng
• Abstract summary: Body shape is one of the significant modality-shared cues for VI-ReID. We propose shape-erased feature learning paradigm that decorrelates modality-shared features in two subspaces. Experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.
• Score: 90.39454748065558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the modality gap between visible and infrared images with high visual ambiguity, learning \textbf{diverse} modality-shared semantic concepts for visible-infrared person re-identification (VI-ReID) remains a challenging problem. Body shape is one of the significant modality-shared cues for VI-ReID. To dig more diverse modality-shared cues, we expect that erasing body-shape-related semantic concepts in the learned features can force the ReID model to extract more and other modality-shared features for identification. To this end, we propose shape-erased feature learning paradigm that decorrelates modality-shared features in two orthogonal subspaces. Jointly learning shape-related feature in one subspace and shape-erased features in the orthogonal complement achieves a conditional mutual information maximization between shape-erased feature and identity discarding body shape information, thus enhancing the diversity of the learned representation explicitly. Extensive experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.

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