Low-light Invariant Representation Learning for Visible-Infrared Person Re-identification

Retrieving target pedestrians from cross-modal images captured by infrared and visible cameras is critical in 24-hour intelligent surveillance. The primary challenge lies in narrowing the modality gap between the visible and infrared modalities. In view of this, existing research tends to extract modality-shared features to bridge the modality gap. However, the extraction process and effectiveness of the shared features are often insufficiently justified. In contrast, we observe that a certain portion of semantics remains invariant across visible and infrared modalities. These invariant semantics provide the basis for extracting modality-shared features. Based on this criterion, we propose a novel method named Low-light Invariant Representation Learning (IRL), which aims to construct an invariant space shared between visible and infrared modalities. Specifically, we introduce a Modality Invariant Extractor, which divides invariance into modality invariance and scale invariance, and extracts the invariant features from different scales and dimensions respectively. Furthermore, a Low-light Representation Enhancement module is designed, which reuses the invariant features and shallow modality features through paired enhancement units and compensation units to highlight cross-modality shared features. Extensive experiments on SYSU-MM01, RegDB, and LLCM benchmarks demonstrate the effectiveness of our method. Code is available https://github.com/Mapzzone/IRL.