Spectral Aware Softmax for Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2302.01512v1
• Date: Fri, 3 Feb 2023 02:57:18 GMT
• Title: Spectral Aware Softmax for Visible-Infrared Person Re-Identification
• Authors: Lei Tan, Pingyang Dai, Qixiang Ye, Mingliang Xu, Yongjian Wu, Rongrong Ji
• Abstract summary: Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Existing methods still follow the softmax loss training paradigm, which is widely used in single-modality classification tasks. We propose the spectral-aware softmax (SA-Softmax) loss, which can fully explore the embedding space with the modality information.
• Score: 123.69049942659285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing methods still follow the softmax loss training paradigm, which is widely used in single-modality classification tasks. The softmax loss lacks an explicit penalty for the apparent modality gap, which adversely limits the performance upper bound of the VI-ReID task. In this paper, we propose the spectral-aware softmax (SA-Softmax) loss, which can fully explore the embedding space with the modality information and has clear interpretability. Specifically, SA-Softmax loss utilizes an asynchronous optimization strategy based on the modality prototype instead of the synchronous optimization based on the identity prototype in the original softmax loss. To encourage a high overlapping between two modalities, SA-Softmax optimizes each sample by the prototype from another spectrum. Based on the observation and analysis of SA-Softmax, we modify the SA-Softmax with the Feature Mask and Absolute-Similarity Term to alleviate the ambiguous optimization during model training. Extensive experimental evaluations conducted on RegDB and SYSU-MM01 demonstrate the superior performance of the SA-Softmax over the state-of-the-art methods in such a cross-modality condition.

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