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Cross-Modality Person Re-Identification

2020CVPR

1	Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
2	Learning Longterm Representations for Person Re-Identification Using Radio Signals
3	Unsupervised Person Re-Identification via Multi-Label Classification
4	Camera On-Boarding for Person Re-Identification Using Hypothesis Transfer Learning
5	Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer
6	Hierarchical Clustering With Hard-Batch Triplet Loss for Person Re-Identification
7	Real-World Person Re-Identification via Degradation Invariance Learning
8	Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
9	Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification
10	AD-Cluster: Augmented DiscriRe-Identification
11	Weakly Supervised Discriminative Feature Learning With State Information for Person Identification
12	High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
13	Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification
14	minative Clustering for Domain Adaptive Person Re-Identification
15	Unity Style Transfer for Person Style Normalization and Restitution for Generalizable Person Re-Identification
16	Relation-Aware Global Attention for Person Re-Identification
17	Salience-Guided Cascaded Suppression Network for Person Re-Identification
18	Unsupervised Person Re-Identification via Softened Similarity Learning
19	COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification
20	Inter-Task Association Critic for Cross-Resolution Person Re-Identification
21	Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification
22	Spatial-Temporal Graph Convolutional Network for Video-Based Person Re-Identification
23	Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-Based Person Re-Identification

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