Domain Adaptive Attention Learning for Unsupervised Person Re-Identification

Abstract

Person re-identification (Re-ID) across multiple datasets is a challenging task due to two main reasons: the presence of large cross-dataset distinctions and the absence of annotated target instances. To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part. In this manner, the domain-shared part is used to capture transferable cues that can compensate cross-dataset distinctions and give positive contributions to the target task, while the domain-specific part aims to model the noisy information to avoid the negative transfer caused by domain diversity. A soft label loss is further employed to take full use of unlabeled target data by estimating pseudo labels. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 benchmarks demonstrate the proposed approach outperforms the state-of-the-arts.

References in corpus (7)

• In Defense of the Triplet Loss for Person Re-Identification
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• Unsupervised Domain Adaptation with Residual Transfer Networks
• Person Re-identification: Past, Present and Future
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Cited by in corpus (9)

• FastReID: A Pytorch Toolbox for General Instance Re-identification
• Domain Adaptive Person Re-Identification via Coupling Optimization
• Exploiting Robust Unsupervised Video Person Re-identification
• Group-aware Label Transfer for Domain Adaptive Person Re-identification
• Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning
• Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
• Person Re-ID through Unsupervised Hypergraph Rank Selection and Fusion
• Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
• Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks