Papers › TransReID: Transformer-based Object Re-Identification

TransReID: Transformer-based Object Re-Identification

8 Feb 2021ICCV 2021 10arXiv:2102.04378archive 2025-07-28

Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, Wei Jiang

Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators (e.g. pooling and strided convolution). To overcome these limitations, we propose a pure transformer-based object ReID framework named TransReID. Specifically, we first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods. To further enhance the robust feature learning in the context of transformers, two novel modules are carefully designed. (i) The jigsaw patch module (JPM) is proposed to rearrange the patch embeddings via shift and patch shuffle operations which generates robust features with improved discrimination ability and more diversified coverage. (ii) The side information embeddings (SIE) is introduced to mitigate feature bias towards camera/view variations by plugging in learnable embeddings to incorporate these non-visual clues. To the best of our knowledge, this is the first work to adopt a pure transformer for ReID research. Experimental results of TransReID are superior promising, which achieve state-of-the-art performance on both person and vehicle ReID benchmarks.

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Code

heshuting555/TransReID officialmentioned in papermentioned on GitHubpytorch report
ChristmasStory/TransReID-main mentioned on GitHubpytorch report
damo-cv/transreid mentioned on GitHubpytorch report
darrishabh/coviprox mentioned on GitHubpytorch report

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Tasks

ObjectPerson Re-IdentificationVehicle Re-Identification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Person Re-Identification DukeMTMC-reID TransReID (w/o RK) Rank-1 91.1 #31 of 94 Archive leaderboard report
Person Re-Identification DukeMTMC-reID TransReID (w/o RK) mAP 82.1 #31 of 94 Archive leaderboard report
Person Re-Identification MSMT17 TransReID Rank-1 86.20 #14 of 43 Archive leaderboard report
Person Re-Identification MSMT17 TransReID mAP 69.40 #14 of 43 Archive leaderboard report
Person Re-Identification Market-1501 TransReID Rank-1 95.2 #65 of 135 Archive leaderboard report
Person Re-Identification Market-1501 TransReID mAP 89.5 #65 of 135 Archive leaderboard report
Person Re-Identification Market-1501-C TransReID Rank-1 53.19 #1 of 22 Archive leaderboard report
Person Re-Identification Market-1501-C TransReID mAP 27.38 #1 of 22 Archive leaderboard report
Person Re-Identification Market-1501-C TransReID mINP 1.98 #1 of 22 Archive leaderboard report
Person Re-Identification Occluded-DukeMTMC TransReID Rank-1 66.4 #16 of 32 Archive leaderboard report
Person Re-Identification Occluded-DukeMTMC TransReID mAP 59.2 #16 of 32 Archive leaderboard report
Vehicle Re-Identification VeRi-776 TransReID Rank-1 97.1 #9 of 17 Archive leaderboard report
Vehicle Re-Identification VeRi-776 TransReID mAP 82.3 #9 of 17 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

Absolute Position EncodingsAdamAttentionBPEConvolutionDense ConnectionsDropoutJigsawLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformerVision Transformer

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