CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification
Haoxuan Xu, Hanzi Wang, Guanglin Niu

TL;DR
This paper introduces a new person re-identification task addressing both clothing changes and modality differences, proposing a benchmark and a novel network to improve cross-modality and clothing-invariant matching.
Contribution
The study defines the CMCC-ReID task, creates the SYSU-CMCC benchmark, and proposes the PIA network with DBDL and BPL modules to handle clothing and modality variations.
Findings
PIA outperforms existing methods on SYSU-CMCC dataset.
The DBDL module effectively disentangles clothing from identity cues.
Bi-Directional Prototype Learning reduces modality gap and clothing interference.
Abstract
Person Re-Identification (ReID) faces severe challenges from modality discrepancy and clothing variation in long-term surveillance scenario. While existing studies have made significant progress in either Visible-Infrared ReID (VI-ReID) or Clothing-Change ReID (CC-ReID), real-world surveillance system often face both challenges simultaneously. To address this overlooked yet realistic problem, we define a new task, termed Cross-Modality Clothing-Change Re-Identification (CMCC-ReID), which targets pedestrian matching across variations in both modality and clothing. To advance research in this direction, we construct a new benchmark SYSU-CMCC, where each identity is captured in both visible and infrared domains with distinct outfits, reflecting the dual heterogeneity of long-term surveillance. To tackle CMCC-ReID, we propose a Progressive Identity Alignment Network (PIA) that progressively…
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