Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification

• URL: http://arxiv.org/abs/2104.11539v1
• Date: Fri, 23 Apr 2021 11:14:07 GMT
• Title: Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification
• Authors: Nianchang Huang, Jianan Liu, Qiang Zhang, Jungong Han
• Abstract summary: Cross-modality person re-identification works rely on discriminative modality-shared features. Despite some initial success, such modality-shared appearance features cannot capture enough modality-invariant information. A novel cross-modality quadruplet loss is proposed to further reduce the cross-modality variations.
• Score: 72.95858515157603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing cross-modality person re-identification works rely on discriminative modality-shared features for reducing cross-modality variations and intra-modality variations. Despite some initial success, such modality-shared appearance features cannot capture enough modality-invariant discriminative information due to a massive discrepancy between RGB and infrared images. To address this issue, on the top of appearance features, we further capture the modality-invariant relations among different person parts (referred to as modality-invariant relation features), which are the complement to those modality-shared appearance features and help to identify persons with similar appearances but different body shapes. To this end, a Multi-level Two-streamed Modality-shared Feature Extraction (MTMFE) sub-network is designed, where the modality-shared appearance features and modality-invariant relation features are first extracted in a shared 2D feature space and a shared 3D feature space, respectively. The two features are then fused into the final modality-shared features such that both cross-modality variations and intra-modality variations can be reduced. Besides, a novel cross-modality quadruplet loss is proposed to further reduce the cross-modality variations. Experimental results on several benchmark datasets demonstrate that our proposed method exceeds state-of-the-art algorithms by a noticeable margin.

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