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Take More Positives: A Contrastive Learning Framework for Unsupervised Person Re-Identification

2021-01-12 08:06:11
Xuanyu He, Wei Zhang, Ran Song, Xiangyuan Lan


tract: Exploring the relationship between examples without manual annotations is a core problem in the field of unsupervised person re-identification (re-ID). In the unsupervised scenario, no ground truth is provided for bringing instances of the same identity closer and spreading samples of different identities apart. In this paper, we introduce a contrastive learning framework for unsupervised person re-ID, which we call Take More Positives (TMP). In an iterative manner, TMP generates pseudo-labels by clustering samples, and updates itself with such pseudo-labels and the proposed contrastive loss. By considering more positive examples, the framework of TMP outperforms the state-of-the-art methods for unsupervised person re-ID. On the Market-1501 benchmark, TMP achieves 88.3% Rank-1 accuracy and 70.4% mean average precision. Our code will be made publicly available.

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3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot