A multi-perturbation consistency framework for semi-supervised person re-identification

The semi-supervised person re-identification(Re-ID) task only manually annotates a small portion of person identities to reduce costs, but existing methods suffer from insufficient and incomplete utilization of hard unlabeled data, which leads to performance bottleneck. In this paper, we propose a new semi-supervised Re-ID framework to address this issue. In this framework, hard unlabeled samples participate in dual feature consistency learning by generating Multi-perturbation views. The proposed multi-perturbations include three different image-level perturbations and one feature-level perturbation, and the combination of these perturbations can fully simulate the complex changes of persons. To further improve the disturbance quality, a semi-supervised image generation network Semi-DGNet and a Perturbation Scheme Generator (PSG) are proposed to enhance the disturbance effect and control the disturbance intensity. Furthermore, a new Quintuplet loss is proposed to further reduce intra-class distance and increase inter-class distance through a metric learning strategy that involves the joint participation of labeled and unlabeled samples. The above work effectively explores the guiding role of labeled samples in training hard unlabeled data, which has inspiring value for future weakly supervised learning research. Extensive experiments on two datasets and sufficient comparisons with other existing state-of-art methods validate the effectiveness of the proposed framework, and verify its successful integration of multiple training strategies and process, modules, and optimization techniques.