  RGB-Infrared person re-identification (RGB-IR ReID) is a challenging
cross-modality retrieval problem, which aims at matching the person-of-interest
over visible and infrared camera views. Most existing works achieve performance
gains through manually-designed feature selection modules, which often require
significant domain knowledge and rich experience. In this paper, we study a
general paradigm, termed Neural Feature Search (NFS), to automate the process
of feature selection. Specifically, NFS combines a dual-level feature search
space and a differentiable search strategy to jointly select identity-related
cues in coarse-grained channels and fine-grained spatial pixels. This
combination allows NFS to adaptively filter background noises and concentrate
on informative parts of human bodies in a data-driven manner. Moreover, a
cross-modality contrastive optimization scheme further guides NFS to search
features that can minimize modality discrepancy whilst maximizing inter-class
distance. Extensive experiments on mainstream benchmarks demonstrate that our
method outperforms state-of-the-arts, especially achieving better performance
on the RegDB dataset with significant improvement of 11.20% and 8.64% in Rank-1
and mAP, respectively.
