Papers › Parameter-Free Spatial Attention Network for Person Re-Identification

Parameter-Free Spatial Attention Network for Person Re-Identification

29 Nov 2018arXiv:1811.12150archive 2025-07-28

Haoran Wang, Yue Fan, Zexin Wang, Licheng Jiao, Bernt Schiele

Global average pooling (GAP) allows to localize discriminative information for recognition [40]. While GAP helps the convolution neural network to attend to the most discriminative features of an object, it may suffer if that information is missing e.g. due to camera viewpoint changes. To circumvent this issue, we argue that it is advantageous to attend to the global configuration of the object by modeling spatial relations among high-level features. We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model. Our spatial attention layer consistently improves the performance over the model without it. Results on four benchmarks demonstrate a superiority of our model over the state-of-the-art achieving rank-1 accuracy of 94.7% on Market-1501, 89.0% on DukeMTMC-ReID, 74.9% on CUHK03-labeled and 69.7% on CUHK03-detected.

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Code

HRanWang/SA mentioned on GitHubpytorchMIT report
HRanWang/Spatial-Attention mentioned on GitHubpytorchMIT report
schizop/SA mentioned on GitHubpytorchMIT report

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Tasks

Person Re-Identification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Person Re-Identification DukeMTMC-reID Parameter-Free Spatial Attention Rank-1 89.0 #20 of 94 Archive leaderboard report
Person Re-Identification DukeMTMC-reID Parameter-Free Spatial Attention mAP 85.9 #20 of 94 Archive leaderboard report
Person Re-Identification Market-1501 Parameter-Free Spatial Attention (RK) Rank-1 94.7 #76 of 135 Archive leaderboard report
Person Re-Identification Market-1501 Parameter-Free Spatial Attention (RK) mAP 91.7 #76 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

Average PoolingConvolution

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