SOTAVerified

Person Re-Identification

Person Re-Identification is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.

Papers

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Showing 876–900 of 1488 papers

TitleStatusHype
Vehicle Re-identification Based on Dual Distance Center Loss—0
Video-Based Convolutional Attention for Person Re-Identification—0
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks—0
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention—0
Video-based Person Re-identification with Accumulative Motion Context—0
Video-based Person Re-identification with Long Short-Term Representation Learning—0
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding—0
Video Person Re-identification by Temporal Residual Learning—0
Video Person Re-identification using Attribute-enhanced Features—0
Video Person Re-Identification using Learned Clip Similarity Aggregation—0
Video Person Re-Identification With Competitive Snippet-Similarity Aggregation and Co-Attentive Snippet Embedding—0
Video Temporal Relationship Mining for Data-Efficient Person Re-identification—0
View Confusion Feature Learning for Person Re-identification—0
ViFi-ReID: A Two-Stream Vision-WiFi Multimodal Approach for Person Re-identification—0
Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning—0
Vista-Morph: Unsupervised Image Registration of Visible-Thermal Facial Pairs—0
Visual Person Understanding through Multi-Task and Multi-Dataset Learning—0
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery—0
VMRFANet:View-Specific Multi-Receptive Field Attention Network for Person Re-identification—0
VRSTC: Occlusion-Free Video Person Re-Identification—0
Weakly Supervised Person Re-Identification—0
Weakly Supervised Tracklet Person Re-Identification by Deep Feature-wise Mutual Learning—0
Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning—0
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification—0
What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification—0
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All datasetsMarket-1501DukeMTMC-reIDMSMT17Occluded-DukeMTMCMarket-1501-CCUHK03 detectedCUHK03 labeledMARSCUHK03LTCCPRCCPRID2011

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1st-ReID(RE, RK)Rank-198—Unverified
2SSKD(GH)Rank-197.36—Unverified
3CLIP-ReID+Pose2ID (no RK)Rank-197.3—Unverified
4SOLIDER +UFFM+AMCRank-197—Unverified
5Unsupervised Pre-training (ResNet101+MGN)Rank-197—Unverified
6RGT&RGPR (RK)Rank-196.9—Unverified
7SOLIDERRank-196.9—Unverified
8LightMBN (RR)Rank-196.8—Unverified
9Viewpoint-Aware Loss(RK)Rank-196.79—Unverified
10SOLIDER (RK)Rank-196.7—Unverified
#ModelMetricClaimedVerifiedStatus
1DenseILmAP97.1—Unverified
2CTL Model (ResNet50, 256x128)mAP96.1—Unverified
3BPBreID (RK)mAP92.9—Unverified
4Unsupervised Pre-training (ResNet101+RK)mAP92.77—Unverified
5st-ReID(RE, RK,Cam)mAP92.7—Unverified
6RGT&RGPR (RK)mAP92.7—Unverified
7Viewpoint-Aware Loss(RK)mAP91.8—Unverified
8LDS (ResNet50 + RK)mAP91—Unverified
9Adaptive L2 Regularization (with re-ranking)mAP90.7—Unverified
10FlipReID (with re-ranking)mAP90.7—Unverified