Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting

Abstract

For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matching directly in deep feature maps. We treat image matching as finding local correspondences in feature maps, and construct query-adaptive convolution kernels on the fly to achieve local matching. In this way, the matching process and results are interpretable, and this explicit matching is more generalizable than representation features to unseen scenarios, such as unknown misalignments, pose or viewpoint changes. To facilitate end-to-end training of this architecture, we further build a class memory module to cache feature maps of the most recent samples of each class, so as to compute image matching losses for metric learning. Through direct cross-dataset evaluation, the proposed Query-Adaptive Convolution (QAConv) method gains large improvements over popular learning methods (about 10%+ mAP), and achieves comparable results to many transfer learning methods. Besides, a model-free temporal cooccurrence based score weighting method called TLift is proposed, which improves the performance to a further extent, achieving state-of-the-art results in cross-dataset person re-identification. Code is available at https://github.com/ShengcaiLiao/QAConv.

arXiv commentThis is the ECCV 2020 version, including the appendix

References in corpus (3)

• Learning Discriminative Features with Multiple Granularities for Person Re-Identification
• End-to-End Comparative Attention Networks for Person Re-identification
• Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding

Cited by in corpus (5)

• Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
• MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification
• Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification
• TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
• CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion