Towards Discriminative Representation Learning for Unsupervised Person Re-identification

Abstract

In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods typically follow a two-stage optimization pipeline, where the network is first pre-trained on source and then fine-tuned on target with pseudo labels created by feature clustering. Such methods sustain two main limitations. (1) The label noise may hinder the learning of discriminative features for recognizing target classes. (2) The domain gap may hinder knowledge transferring from source to target. We propose three types of technical schemes to alleviate these issues. First, we propose a cluster-wise contrastive learning algorithm (CCL) by iterative optimization of feature learning and cluster refinery to learn noise-tolerant representations in the unsupervised manner. Second, we adopt a progressive domain adaptation (PDA) strategy to gradually mitigate the domain gap between source and target data. Third, we propose Fourier augmentation (FA) for further maximizing the class separability of re-ID models by imposing extra constraints in the Fourier space. We observe that these proposed schemes are capable of facilitating the learning of discriminative feature representations. Experiments demonstrate that our method consistently achieves notable improvements over the state-of-the-art unsupervised re-ID methods on multiple benchmarks, e.g., surpassing MMT largely by 8.1\%, 9.9\%, 11.4\% and 11.1\% mAP on the Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT tasks, respectively.

ICCV 2021

References in corpus (13)

• Learning Transferable Features with Deep Adaptation Networks
• Domain Separation Networks
• Adversarial Discriminative Domain Adaptation
• Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
• TransReID: Transformer-based Object Re-Identification
• Unsupervised Person Re-identification by Soft Multilabel Learning
• Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification
• AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
• Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
• Linkage Based Face Clustering via Graph Convolution Network
• Group-aware Label Transfer for Domain Adaptive Person Re-identification
• Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification