Unsupervised person re-identification with multi-label learning guided self-paced clustering

Highlights：

• We propose a conceptually novel yet simple framework termed Multi-label Learning guided self-paced Clustering, to address the unsupervised person Re-ID problem.

• Our framework provides better unsupervised discriminative features with three crucial modules, namely a multi-scale network which obtains global and local person representations, a multi-label learning module which trains the network with memory bank and multi-label classification loss, and a self-paced clustering module which removes noisy samples and assigns pseudo labels for training.

• Extensive experiments on three challenging large-scale datasets demonstrated the effectiveness of all the modules. Our framework finally achieves state-of-the-art performance on these datasets.