Exploiting reliable pseudo-labels for unsupervised domain adaptive person re-identification

Abstract

Person re-identification, getting impressive performance under the single-domain setting, often suffers huge performance drop when deploying to the unseen target domain owing to domain gap. Current research mainly focuses on unsupervised domain adaptation to alleviate the domain gap, and the methods by clustering the target-domain samples have achieved significant results. However, some inaccurate pseudo-labels, i.e., noisy pseudo-labels, may be generated on clustering, which will seriously affect the performance of the model. In order to solve the above problem, we propose a novel unsupervised domain adaptive person re-identification method by exploiting reliable pseudo-labels (RPL) from two aspects, i.e., adaptive dynamic clustering (ADC) and cross-camera similarity evaluation (CCSE). Specifically, firstly, for the methods based on the density-based clustering algorithm, we propose the adaptive dynamic clustering which calculates the clustering radius adaptively and dynamically to obtain more reasonable clustering results in the iterative optimization of the model. Next, for noisy pseudo-labels caused by small inter-class variations under the same camera, we propose the cross-camera similarity evaluation to filter out these noises to further improve the discrimination of the model. Extensive experiments on three publicly available large-scale datasets show that the proposed method can achieve state-of-the-art performance on unsupervised domain adaptation person re-identification.