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MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification by Gao, Yajun; Liang, Tengfei; Jin, Yi; Gu, Xiaoyan; Liu, Wu; Li, Yidong; Lang, Congyan is a scholarly article available to read on EtoBox.

What is MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification about?

The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality. Existing methods mainly use a two-stream architecture to eliminate the discrepancy between the two modalities in the final common feature space, which ignore the single space of each modality in the shallow layers. To solve it, in this paper, we present a novel multi-feature space joint optimization (MSO) network, which can learn modality-sharable features in both the single-modality space and the common space. Firstly, based on the observation that edge information is modality-invariant, we propose an edge features enhancement module to enhance the modality-sharable features in each single-modality space. Specifically, we design a perceptual edge features (PEF) loss after the edge fusion strategy analysis. According to our knowledge, this is the first work that proposes explicit optimization in the single-modality feature space on cross-modality ReID task. Moreover, to increase the difference between cross-modality distance and class distance, we introduce a novel cross-modality contrastive-center (CMCC) loss

Author

Gao, Yajun; Liang, Tengfei; Jin, Yi; Gu, Xiaoyan; Liu, Wu; Li, Yidong; Lang, Congyan

Published

2021

Language

EN