Decoupled Contrastive Learning for Intra-Camera Supervised Person Re-identification
Shiteng Hu, Xin Zhang, Xiaohua Xie · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Intra-camera supervised (ICS) person re-identification (Re-ID) assumes that people are annotated independently in each camera, which is a newly proposed setting to reduce the cost of manual annotation. Most existing methods are developed on generating global pseudo labels to learn camera-agnostic features by classification loss. However, they do not utilize intra-camera labels well for cross-camera association. In this paper, we propose a Decoupled Contrastive Learning (DCL) strategy to tackle the issue. Concretely, we first conduct an intra-camera pre-train stage to reduce the intra-identity variance. Then an inter-identity association and an inter-camera learning step are alternatively iterated to improve the feature representation gradually. We propose a decoupled identity contrastive loss in the inter-camera learning stage to make the training more effective. In order to improve the compactness of the associated cluster, we further adopt a hard-aware contrastive loss. Extensive experiments on two large-scale Re-ID datasets demonstrate that the proposed method outperforms most ICS methods and performs comparably to fully supervised methods.