Papers › A Deep Hierarchical Feature Sparse Framework for Occluded Person Re-Identification

A Deep Hierarchical Feature Sparse Framework for Occluded Person Re-Identification

15 Jan 2024arXiv:2401.07469archive 2025-07-28

Yihu Song, Shuaishi Liu

Most existing methods tackle the problem of occluded person re-identification (ReID) by utilizing auxiliary models, resulting in a complicated and inefficient ReID framework that is unacceptable for real-time applications. In this work, a speed-up person ReID framework named SUReID is proposed to mitigate occlusion interference while speeding up inference. The SUReID consists of three key components: hierarchical token sparsification (HTS) strategy, non-parametric feature alignment knowledge distillation (NPKD), and noise occlusion data augmentation (NODA). The HTS strategy works by pruning the redundant tokens in the vision transformer to achieve highly effective self-attention computation and eliminate interference from occlusions or background noise. However, the pruned tokens may contain human part features that contaminate the feature representation and degrade the performance. To solve this problem, the NPKD is employed to supervise the HTS strategy, retaining more discriminative tokens and discarding meaningless ones. Furthermore, the NODA is designed to introduce more noisy samples, which further trains the ability of the HTS to disentangle different tokens. Experimental results show that the SUReID achieves superior performance with surprisingly fast inference.

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Tasks

Data AugmentationKnowledge DistillationOccluded Person Re-IdentificationPerson Re-Identification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Person Re-Identification Occluded-DukeMTMC SUReID Rank-1 65.8 #17 of 32 Archive leaderboard report
Person Re-Identification Occluded-DukeMTMC SUReID mAP 54.9 #17 of 32 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

AttentionDense ConnectionsKnowledge DistillationLayer NormalizationLinear LayerMulti-Head AttentionPruningResidual ConnectionSoftmaxVision Transformer

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