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 1101–1150 of 1488 papers

TitleStatusHype
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding—0
Deep Multi-Index Hashing for Person Re-Identification—0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identificationCode0
Additive Adversarial Learning for Unbiased AuthenticationCode0
Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation—0
DotSCN: Group Re-identification via Domain-Transferred Single and Couple Representation Learning—0
Illumination-Adaptive Person Re-identification—0
Towards Egocentric Person Re-identification and Social Pattern Analysis—0
Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in PracticeCode0
Improved Hard Example Mining by Discovering Attribute-based Hard Person Identity—0
Intra-clip Aggregation for Video Person Re-identification—0
Appearance and Pose-Conditioned Human Image Generation using Deformable GANsCode0
Deep Constrained Dominant Sets for Person Re-identificationCode0
Multi-Scale Body-Part Mask Guided Attention for Person Re-identification—0
Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting—0
An Introduction to Person Re-identification with Generative Adversarial Networks—0
Person Re-identification with Metric Learning using Privileged Information—0
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification—0
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification—0
Convolutional Temporal Attention Model for Video-based Person Re-identification—0
Weakly Supervised Person Re-Identification—0
Adaptively Connected Neural NetworksCode0
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification—0
Re-Identification Supervised Texture Generation—0
Relation-Aware Global Attention for Person Re-identificationCode0
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identificationCode0
Cross-Entropy Adversarial View Adaptation for Person Re-identification—0
Learning Context Graph for Person Search—0
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification—0
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identificationCode0
Pedestrian re-identification based on Tree branch network with local and global learning—0
Person Re-identification with Bias-controlled Adversarial Training—0
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification—0
GAN-based Pose-aware Regulation for Video-based Person Re-identification—0
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-IdentificationCode0
STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification—0
Unsupervised Person Re-identification by Soft Multilabel LearningCode0
Inserting Videos into Videos—0
Learning Feature Aggregation in Temporal Domain for Re-Identification—0
Unsupervised Tracklet Person Re-IdentificationCode0
Attributes-aided Part Detection and Refinement for Person Re-identification—0
2017 Robotic Instrument Segmentation ChallengeCode0
Person Re-identification in Videos by Analyzing Spatio-Temporal Tubes—0
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 Systems—0
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-IdentificationCode0
Ensemble Feature 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