EANet: Enhancing Alignment for Cross-Domain Person Re-identification

Abstract

Person re-identification (ReID) has achieved significant improvement under the single-domain setting. However, directly exploiting a model to new domains is always faced with huge performance drop, and adapting the model to new domains without target-domain identity labels is still challenging. In this paper, we address cross-domain ReID and make contributions for both model generalization and adaptation. First, we propose Part Aligned Pooling (PAP) that brings significant improvement for cross-domain testing. Second, we design a Part Segmentation (PS) constraint over ReID feature to enhance alignment and improve model generalization. Finally, we show that applying our PS constraint to unlabeled target domain images serves as effective domain adaptation. We conduct extensive experiments between three large datasets, Market1501, CUHK03 and DukeMTMC-reID. Our model achieves state-of-the-art performance under both source-domain and cross-domain settings. For completeness, we also demonstrate the complementarity of our model to existing domain adaptation methods. The code is available at https://github.com/huanghoujing/EANet.

References in corpus (5)

• In Defense of the Triplet Loss for Person Re-Identification
• Person Re-identification: Past, Present and Future
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval
• Dual Attention Network for Scene Segmentation

Cited by in corpus (11)

• Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification
• Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
• Self-training with progressive augmentation for unsupervised cross-domain person re-identification
• Attention: A Big Surprise for Cross-Domain Person Re-Identification
• Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
• Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization
• Memory Regulation and Alignment toward Generalizer RGB-Infrared Person
• PGGANet: Pose Guided Graph Attention Network for Person Re-identification
• 3D Human Texture Estimation from a Single Image with Transformers
• MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification
• Psychophysical Evaluation of Deep Re-Identification Models