Papers › Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Zhihui Zhu, Xinyang Jiang, Feng Zheng, Xiaowei Guo, Feiyue Huang, Wei-Shi Zheng, Xing Sun
Although great progress in supervised person re-identification (Re-ID) has been made recently, due to the viewpoint variation of a person, Re-ID remains a massive visual challenge. Most existing viewpoint-based person Re-ID methods project images from each viewpoint into separated and unrelated sub-feature spaces. They only model the identity-level distribution inside an individual viewpoint but ignore the underlying relationship between different viewpoints. To address this problem, we propose a novel approach, called \textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}). Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level. In addition, rather than modeling different viewpoints as hard labels used for conventional viewpoint classification, we introduce viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation. VALSR can effectively solve the ambiguity of the viewpoint cluster label assignment. Extensive experiments on the Market1501 and DukeMTMC-reID datasets demonstrated that our method outperforms the state-of-the-art supervised Re-ID methods.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Person Re-Identification | DukeMTMC-reID | Viewpoint-Aware Loss(RK) | Rank-1 | 93.9 | #7 of 94 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | Viewpoint-Aware Loss(RK) | Rank-5 | 96.5 | #7 of 94 | Archive leaderboard | report |
| Person Re-Identification | DukeMTMC-reID | Viewpoint-Aware Loss(RK) | mAP | 91.8 | #7 of 94 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | Viewpoint-Aware Loss(RK) | Rank-1 | 96.79 | #9 of 135 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | Viewpoint-Aware Loss(RK) | Rank-5 | 98.31 | #9 of 135 | Archive leaderboard | report |
| Person Re-Identification | Market-1501 | Viewpoint-Aware Loss(RK) | mAP | 95.43 | #9 of 135 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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