Unsupervised Person Re-Identification Via Nearest Neighbor Collaborative Training Strategy

Because of the lack of human-labeled data, the challenge of unsupervised person re-identification (re-ID) is to learn to generate correct pseudo labels for training. Unlike the human-labeled annotation, the generated pseudo labels contain the noise labels that harm the model's performance. In this paper, we propose the Nearest Neighbors Collaborative Training (NNCT) strategy to mitigate the effects of noisy labels by utilizing information of the nearest neighbor of an image. The proposed NNCT trains the image and its nearest neighbor collaboratively, thereby enhancing the generalization capability of the network and shortening the distance with neighbors. To make training using the up-to-date nearest neighbor possible, we introduce a Pseudo Label Memory Bank (PLMB) to store the up-to-date labels of all images. The experimental results confirm the superiority of the proposed method, which surpasses state-of-the-arts on two mainstream person re-ID datasets, Market-1501, and DukeMTMC-reID in both fully unsupervised learning manner and Unsupervised Domain Adaptation (UDA) manner.