Multi-Scale Query-Adaptive Convolution for Generalizable Person Re-Identification

Abstract

Domain Generalization in person re-identification (ReID) aims to learn a generalizable model from a single or multi-source domain that can be directly deployed to an unseen domain without fine-tuning. In this paper, we investigate the problem of single-source domain generalization in ReID. Recent research has gained remarkable progress by treating image matching as a search for local correspondences in feature maps. However, to ensure efficient matching, they usually adopt a pixel-wise matching approach, which is prone to be deviated by the identity-irrelevant patch features in the image, such as background patches. To address this problem, we propose the Multi-scale Query-Adaptive Convolution (QAConv-MS) framework. Specifically, we adopt a group of template kernels with different scales to extract local features of different receptive fields from the original feature maps and accordingly perform the local matching process. We also introduce a self-attention branch to extract global features from the feature map as complementary information for local features. Our approach achieves state-of-the-art performances on four large-scale datasets.