Intra-Inter Camera Similarity for Unsupervised Person Re-Identification

CVPR 2021  ·  Shiyu Xuan, Shiliang Zhang ·

Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by measuring the feature similarity without considering the distribution discrepancy among cameras, leading to degraded accuracy in label computation across cameras. This paper targets to address this challenge by studying a novel intra-inter camera similarity for pseudo-label generation. We decompose the sample similarity computation into two stage, i.e., the intra-camera and inter-camera computations, respectively. The intra-camera computation directly leverages the CNN features for similarity computation within each camera. Pseudo-labels generated on different cameras train the re-id model in a multi-branch network. The second stage considers the classification scores of each sample on different cameras as a new feature vector. This new feature effectively alleviates the distribution discrepancy among cameras and generates more reliable pseudo-labels. We hence train our re-id model in two stages with intra-camera and inter-camera pseudo-labels, respectively. This simple intra-inter camera similarity produces surprisingly good performance on multiple datasets, e.g., achieves rank-1 accuracy of 89.5% on the Market1501 dataset, outperforming the recent unsupervised works by 9+%, and is comparable with the latest transfer learning works that leverage extra annotations.

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Results from the Paper

 Ranked #1 on Person Re-Identification on SYSU-30k (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Unsupervised Person Re-Identification DukeMTMCreID IICS Rank-1 80.0 # 1
Unsupervised Person Re-Identification Market-1501 IICS Rank-1 89.5 # 16
MAP 72.9 # 15
Rank-10 97.0 # 12
Rank-5 95.2 # 12
Person Re-Identification SYSU-30k IICS (generalization) Rank-1 36.0 # 1


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