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

Showing 11261150 of 1488 papers

TitleStatusHype
Unsupervised Tracklet Person Re-IdentificationCode0
Attributes-aided Part Detection and Refinement for Person Re-identification0
2017 Robotic Instrument Segmentation ChallengeCode0
Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes0
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Unsupervised Person Re-identification by Deep Asymmetric Metric EmbeddingCode0
Discovering Underlying Person Structure Pattern with Relative Local Distance for Person Re-identificationCode0
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems0
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-IdentificationCode0
Ensemble Feature for Person Re-Identification0
Attribute-Aware Attention Model for Fine-grained Representation LearningCode0
EANet: Enhancing Alignment for Cross-Domain Person Re-identificationCode0
Spatial and Temporal Mutual Promotion for Video-based Person Re-identificationCode0
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification0
EgoReID Dataset: Person Re-identification in Videos Acquired by Mobile Devices with First-Person Point-of-View0
Cluster Loss for Person Re-Identification0
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection0
Densely Semantically Aligned Person Re-Identification0
Learning Incremental Triplet Margin for Person Re-identification0
Deep Active Learning for Video-based Person Re-identification0
Omni-directional Feature Learning for Person Re-identification0
Spatial-Temporal Person Re-identificationCode0
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation0
Fast and Accurate Person Re-Identification with RMNetCode0
Dissecting Person Re-identification from the Viewpoint of ViewpointCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1st-ReID(RE, RK)Rank-198Unverified
2SSKD(GH)Rank-197.36Unverified
3CLIP-ReID+Pose2ID (no RK)Rank-197.3Unverified
4SOLIDER +UFFM+AMCRank-197Unverified
5Unsupervised Pre-training (ResNet101+MGN)Rank-197Unverified
6RGT&RGPR (RK)Rank-196.9Unverified
7SOLIDERRank-196.9Unverified
8LightMBN (RR)Rank-196.8Unverified
9Viewpoint-Aware Loss(RK)Rank-196.79Unverified
10TransReID-SSL (ViT-B w/o RK)Rank-196.7Unverified
#ModelMetricClaimedVerifiedStatus
1DenseILmAP97.1Unverified
2CTL Model (ResNet50, 256x128)mAP96.1Unverified
3BPBreID (RK)mAP92.9Unverified
4Unsupervised Pre-training (ResNet101+RK)mAP92.77Unverified
5RGT&RGPR (RK)mAP92.7Unverified
6st-ReID(RE, RK,Cam)mAP92.7Unverified
7Viewpoint-Aware Loss(RK)mAP91.8Unverified
8LDS (ResNet50 + RK)mAP91Unverified
9Adaptive L2 Regularization (with re-ranking)mAP90.7Unverified
10FlipReID (with re-ranking)mAP90.7Unverified