Deep Constraints Space via Channel Alignment for Visible-Infrared Person Re-identification

作     者：Qin, Wencheng Huang, Baojin Huang, Zhiyong Tahsin, Lamia Sun, Daming 

作者机构：Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Engn Res Ctr Med Elect & informat techno Chongqing 400044 Peoples R China Univ Asia Pacific Dept Comp Sci & Engn Dhaka 1205 Bangladesh 

出 版 物：《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期：2022年第29卷

页      面：2672-2676页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Key R&D Program of China [2021YFC2009200] 

主　　题：Semantics Residual neural networks Signal processing algorithms Training Indexes Clustering algorithms Task analysis Visible-Infrared person re-identification channel alignment hierarchically-aware channel clusters inter-modality 

摘      要：Reducing the inter-modality gap has been the core of visible-infrared person re-identification (VI-ReID). Most of the existing methods directly constrain the features to reduce the gap between the different modalities. However, the consistency of channel semantic information across different modalities is ignored. In this letter, channel alignment along with severe semantic constraints is proposed to alleviate the significant distribution differences between modalities. To obtain the optimum channel matching mode, a novel channel alignment mechanism termed Channel Instance Level Alignment (CILA) is proposed at the shallow layer, and matched channel features are constrained by the proposed Channel Instance Alignment (CIA) Loss. In the middle layer, we divide the features into multiple hierarchically-aware channel clusters and align the channel clusters by Channel Cluster Alignment (CCA) Loss. The proposed method is validated on SYSU-MM01 and RegDB, and extensive experiments show that the proposed method achieves competitive performance compared with the state of the arts.