Unsupervised Person Re-identification by Soft Multilabel Learning

Abstract

Although unsupervised person re-identification (RE-ID) has drawn increasing research attentions due to its potential to address the scalability problem of supervised RE-ID models, it is very challenging to learn discriminative information in the absence of pairwise labels across disjoint camera views. To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID. The idea is to learn a soft multilabel (real-valued label likelihood vector) for each unlabeled person by comparing (and representing) the unlabeled person with a set of known reference persons from an auxiliary domain. We propose the soft multilabel-guided hard negative mining to learn a discriminative embedding for the unlabeled target domain by exploring the similarity consistency of the visual features and the soft multilabels of unlabeled target pairs. Since most target pairs are cross-view pairs, we develop the cross-view consistent soft multilabel learning to achieve the learning goal that the soft multilabels are consistently good across different camera views. To enable effecient soft multilabel learning, we introduce the reference agent learning to represent each reference person by a reference agent in a joint embedding. We evaluate our unified deep model on Market-1501 and DukeMTMC-reID. Our model outperforms the state-of-the-art unsupervised RE-ID methods by clear margins. Code is available at https://github.com/KovenYu/MAR.

CVPR19, oral

References in corpus (5)

• In Defense of the Triplet Loss for Person Re-Identification
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• Person Re-identification: Past, Present and Future
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Domain Adaptation for Visual Applications: A Comprehensive Survey

Cited by in corpus (10)

• Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
• Domain Adaptive Person Re-Identification via Coupling Optimization
• Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification
• Unsupervised Person Re-identification via Softened Similarity Learning
• Style Normalization and Restitution for Generalizable Person Re-identification
• Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
• Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples
• ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
• Towards Discriminative Representation Learning for Unsupervised Person Re-identification
• Weakly supervised discriminative feature learning with state information for person identification