Fully Unsupervised Person Re-Identification via Multiple Pseudo Labels Joint Training

Abstract

Person re-identification (re-ID) is the task of finding the matched person in a non-overlapping and multi-camera system. Because annotating images across multiple cameras is difficult and time-consuming, this paper focuses on fully unsupervised learning person re-ID that can learn person re-ID on unlabeled data. The unsupervised re-ID needs to self-generate pseudo labels to make training possible. Unlike human-annotated true labels, the pseudo labels contain noise labels which substantially hinder the network’s capability on feature learning. In order to refine the predicted pseudo labels, we introduce a novel unsupervised re-ID method named Multiple pseudo Labels Joint Training (MLJT) in this paper. Different from the existing works, the MLJT predicts multiple pseudo labels for each image by mining potential similarities in multiple ways. Based on invariance constraints among multiple pseudo labels, the MLJT is jointly optimized under the supervision of multiple pseudo labels to ease the impact of noises in the single pseudo label. The proposed MLJT predicts three types of pseudo labels for one input image. The first one is the clustering-based pseudo label. The second one is adaptive similarity measurement-based pseudo label. The third one is pseudo sub-labels which are predicted by mining channel-based self-similarities. The proposed MLJT has been extensively evaluated on two mainstream and public person re-ID datasets and outdoor real-world videos. Experiments demonstrate the effectiveness of the proposed multiple pseudo labels joint training strategy and the practicality of the proposed MLJT in real-world unsupervised person re-ID applications. The testing demo can be found at https://drive.google.com/drive/folders/1RvNaEiy6tF18_RcgTNcjE7jJ6eGy8sZL?usp=sharing .