Plausible Proxy Mining With Credibility for Unsupervised Person Re-Identification

One effective way to address unsupervised person re-identification is to use a clustering-based contrastive learning approach. Existing state-of-the-art methods adopt clustering algorithms (e.g., DBSCAN) and camera ID information to divide all person images into several camera-aware proxies. Then, for each person image, the extracted feature representation is pulled closer to the centroids of its pseudo-positive proxies (the proxies that share the same pseudo-identity label with this image) and pushed away from the centroids of other pseudo-negative proxies (the proxies that share the different pseudo-identity label with this image). However, the quality of the proxy centroid is significantly affected by the proxy impurity issue and thus deteriorates the learned feature representations. On the premise that we cannot introduce superior supervision signals by thoroughly solving the proxy impurity issue, for a person image, identifying its plausible proxies: the pseudo-negative proxies which potentially include its wrongly-clustered instances (the instances with the same ground-truth identity with this image), and further fixing the resulted incorrect supervision signals become an urgent and challenging problem. This paper proposes a simple yet effective approach to address this problem. With a given image, our method can effectively locate its plausible proxies. Then we introduce credibility to measure how much we should treat the centroid of each mined plausible proxy as a positive supervision signal rather than entirely negative. Extensive experiments on three widely-used person re-ID datasets validate the effectiveness of our proposed approach. Codes will be available at: https://github.com/Dingyuan-Zheng/PPCL.

I. Introduction

person re-identification (re-ID) aims to retrieve pedestrians with the same identity as the given probe image across non-overlapping camera views. To meet increasing demands in urban security, person re-ID is an essential intelligent video surveillance technique. Relying on video surveillance systems widely deployed in railway stations, subway stations, supermarkets, and other crowded public places, person re-ID technology is of significant importance in improving the efficiency of tracing criminal suspects, crime prevention, and missing population investigation. Traditional fully-supervised works achieve impressive performance, where all identity annotations are available during training [1], [2], [3], [4]. Such methods, however, heavily rely on manually-labelled person identity annotations, which are pretty costly to collect. Thus, it becomes formidable to apply these fully-supervised methods into large-scale practical scenarios, where many person images need to be annotated in a limited time. To address this issue, the unsupervised re-ID methods, without the need for identity annotations during training, have been studied by an increasing number of researchers [5], [6], [7], [8].