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Unsupervised cross-domain person re-identification (Re-ID) aims to adapt theinformation from the labelled source domain to an unlabelled target domain. Dueto the lack of supervision in the target domain, it is crucial to identify theunderlying similarity-and-dissimilarity relationships among the unlabelledsamples in the target domain. In order to use the whole data relationshipsefficiently in mini-batch training, we apply a series of memory modules tomaintain an up-to-date representation of the entire dataset. Unlike the simpleexemplar memory in previous works, we propose a novel multi-level memorynetwork (MMN) to discover multi-level complementary information in the targetdomain, relying on three memory modules, i.e., part-level memory,instance-level memory, and domain-level memory. The proposed memory modulesstore multi-level representations of the target domain, which capture both thefine-grained differences between images and the global structure for theholistic target domain. The three memory modules complement each other andsystematically integrate multi-level supervision from bottom to up. Experimentson three datasets demonstrate that the multi-level memory modules cooperativelyboost the unsupervised cross-domain Re-ID task, and the proposed MMN achievescompetitive results.

Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory