ACML: Attention-Based Cross-Modality Learning For Cloth-Changing and Occluded Person Re-Identification

Person Re-Identification (Re-ID) aims at matching a person captured by a non-overlapping camera system. Real-world Re-ID presents challenges like clothing changes and occlusions, which limits the applicability of traditional appearance-based methods. Cloth-Changing Re-ID (CCRe-ID) methods that rely on cloth-invariant modalities, such as shape, gait, etc., ignore occlusions and fail to mine the complementary relationship across modalities. Meanwhile, methods that explicitly focus on occlusion management struggle with cloth-changing scenarios. To address these, we propose ACML: Attention-based Cross-Modality Learning, the first framework to tackle both clothing changes and occlusion in Re-ID. Our lightweight framework comprises a unified network with cascaded Cross-Attention Blocks that extracts appearance and shape features collaboratively, enhancing robustness under clothing changes, viewpoint variations, and poor illumination conditions. Inputs to the network are produced by our novel occlusion synthesis module, which not only helps exposing the model to occlusions but also guides the model to adaptively attend to informative cues and reduce noise. Experiments demonstrate the effectiveness of ACML on both CCRe-ID and occluded Re-ID datasets.