Hierarchical Camera-Aware Contrast Extension for Unsupervised Person Re-Identification

Xi Luo, Min Jiang, Jun Kong, Xuefeng Tao

Abstract: Unsupervised person re-identification (Re-ID) targets to learn discriminative representations without annotations. Recently, clustering-based methods have shown promising performance, which utilize clustering to generate identity pseudo labels for model optimization. Large intra-class variance mainly caused by domain discrepancy among cameras could lead to noisy clustering results. However, abundant camera-aware sample pairs relations have not been exploited fully to facilitate learning of features with comprehensive knowledge, so as to tackle this issue. In this paper, we propose hierarchical camera-aware contrast extension (HCACE) for unsupervised person Re-ID. Firstly, cognitive collaboration contrast scheme (CCCS) is introduced to explore hierarchical camera-aware relations at the proxy-level, so as to collaboratively promote model to learn representative knowledge. Secondly, aggregative instance contrast extension scheme (AICES) is proposed to promote the learning of potential fine-grained knowledge by aggregating refined camera-aware inter-instance relations. Especially in AICES, hard negative instance extension (HNIE) is designed to generate extended negative instances, so as to assist the exploration of transitional cross-camera inter-instance relations. Finally, extensive experiments on three benchmark datasets validate superior performance of proposed HCACE.