Papers › Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification
Vuong D. Nguyen, Khadija Khaldi, Dung Nguyen, Pranav Mantini, Shishir Shah
Traditional approaches for Person Re-identification (Re-ID) rely heavily on modeling the appearance of persons. This measure is unreliable over longer durations due to the possibility for changes in clothing or biometric information. Furthermore, viewpoint changes significantly degrade the matching ability of these methods. In this paper, we propose "Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification" (CVSL) to address these challenges. Our method robustly extracts local and global texture-invariant human body shape cues from 2D pose using the Relational Shape Embedding branch, which consists of a pose estimator and a shape encoder built on a Graph Attention Network. To enhance the discriminability of the shape and appearance of identities under viewpoint variations, we propose Contrastive Viewpoint-aware Losses (CVL). CVL leverages contrastive learning to simultaneously minimize the intra-class gap under different viewpoints and maximize the inter-class gap under the same viewpoint. Extensive experiments demonstrate that our proposed framework outperforms state-of-the-art methods on long-term person Re-ID benchmarks.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections