G$^2$DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2106.07853v1
• Date: Tue, 15 Jun 2021 03:14:31 GMT
• Title: G$^2$DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification
• Authors: Lin Wan, Zongyuan Sun, Qianyan Jing, Yehansen Chen, Lijing Lu, and Zhihang Li
• Abstract summary: RGB-IR person re-identification aims to retrieve person-of-interest between heterogeneous modalities. This paper presents a Geometry-Guided Dual-Alignment learning framework (G$2$DA) to tackle sample-level modality difference.
• Score: 3.909938091041451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RGB-Infrared (IR) person re-identification aims to retrieve person-of-interest between heterogeneous modalities, suffering from large modality discrepancy caused by different sensory devices. Existing methods mainly focus on global-level modality alignment, whereas neglect sample-level modality divergence to some extent, leading to performance degradation. This paper attempts to find RGB-IR ReID solutions from tackling sample-level modality difference, and presents a Geometry-Guided Dual-Alignment learning framework (G$^2$DA), which jointly enhances modality-invariance and reinforces discriminability with human topological structure in features to boost the overall matching performance. Specifically, G$^2$DA extracts accurate body part features with a pose estimator, serving as a semantic bridge complementing the missing local details in global descriptor. Based on extracted local and global features, a novel distribution constraint derived from optimal transport is introduced to mitigate the modality gap in a fine-grained sample-level manner. Beyond pair-wise relations across two modalities, it additionally measures the structural similarity of different parts, thus both multi-level features and their relations are kept consistent in the common feature space. Considering the inherent human-topology information, we further advance a geometry-guided graph learning module to refine each part features, where relevant regions can be emphasized while meaningless ones are suppressed, effectively facilitating robust feature learning. Extensive experiments on two standard benchmark datasets validate the superiority of our proposed method, yielding competitive performance over the state-of-the-art approaches.

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