Domain generalization (DG) serves as a promising solution to handle person
Re-Identification (Re-ID), which trains the model using labels from the source
domain alone, and then directly adopts the trained model to the target domain
without model updating. However, existing DG approaches are usually disturbed
by serious domain variations due to significant dataset variations.
Subsequently, DG highly relies on designing domain-invariant features, which is
however not well exploited, since most existing approaches directly mix
multiple datasets to train DG based models without considering the local
dataset similarities, i.e., examples that are very similar but from different
domains. In this paper, we present a Dual Distribution Alignment Network
(DDAN), which handles this challenge by mapping images into a domain-invariant
feature space by selectively aligning distributions of multiple source domains.
Such an alignment is conducted by dual-level constraints, i.e., the domain-wise
adversarial feature learning and the identity-wise similarity enhancement. We
evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID)
benchmark. Quantitative results demonstrate that the proposed DDAN can well
align the distributions of various source domains, and significantly
outperforms all existing domain generalization approaches.

本文介绍了一种双分布对齐网络 (DDAN)，通过选择性对齐多个源域的分布将图像映射到域不变特征空间，然后通过双层约束，即面向域的对抗特征学习和面向身份的相似性增强来处理人员重识别中的域泛化问题。量化结果表明，在大规模域泛化人员重识别基准测试中，DDAN 可以很好地对齐各种源域的分布，并显着优于所有现有的域泛化方法。