Multi-scale Semantic Correlation Mining for Visible-Infrared Person
  Re-Identification
Ke Cheng Xuecheng Hua Hu Lu Juanjuan Tu Yuanquan Wang Shitong Wang
The main challenge in the Visible-Infrared Person Re-Identification (VI-ReID)
task lies in how to extract discriminative features from different modalities
for matching purposes. While the existing well works primarily focus on
minimizing the modal discrepancies, the modality information can not thoroughly
be leveraged. To solve this problem, a Multi-scale Semantic Correlation Mining
network (MSCMNet) is proposed to comprehensively exploit semantic features at
multiple scales and simultaneously reduce modality information loss as small as
possible in feature extraction. The proposed network contains three novel
components. Firstly, after taking into account the effective utilization of
modality information, the Multi-scale Information Correlation Mining Block
(MIMB) is designed to explore semantic correlations across multiple scales.
Secondly, in order to enrich the semantic information that MIMB can utilize, a
quadruple-stream feature extractor (QFE) with non-shared parameters is
specifically designed to extract information from different dimensions of the
dataset. Finally, the Quadruple Center Triplet Loss (QCT) is further proposed
to address the information discrepancy in the comprehensive features. Extensive
experiments on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that the
proposed MSCMNet achieves the greatest accuracy.