Pro-ReID: Producing reliable pseudo labels for unsupervised person re-identification

Abstract

Mainstream unsupervised person ReIDentification (ReID) is on the basis of the alternation of clustering and fine-tuning to promote the task performance, but the clustering process inevitably produces noisy pseudo labels, which seriously constrains the further advancement of the task performance. To conquer the above concerns, the novel Pro-ReID framework is proposed to produce reliable person samples from the pseudo-labeled dataset to learn feature representations in this work. It consists of two modules: Pseudo Labels Correction (PLC) and Pseudo Labels Selection (PLS). Specifically, we further leverage the temporal ensemble prior knowledge to promote task performance. The PLC module assigns corresponding soft pseudo labels to each sample with control of soft pseudo label participation to potentially correct for noisy pseudo labels generated during clustering; the PLS module associates the predictions of the temporal ensemble model with pseudo label annotations and it detects noisy pseudo labele examples as out-of-distribution examples through the Gaussian Mixture Model (GMM) to supply reliable pseudo labels for the unsupervised person ReID task in consideration of their loss data distribution. Experimental findings validated on three person (Market-1501, DukeMTMC-reID and MSMT17) and one vehicle (VeRi-776) ReID benchmark establish that the novel Pro-ReID framework achieves competitive performance, in particular the mAP on the ambitious MSMT17 that is 4.3% superior to the state-of-the-art methods.