Hybrid Perturbation Strategy for Semi-Supervised Crowd Counting.

Abstract

A simple yet effective semi-supervised method is proposed in this paper based on consistency regularization for crowd counting, and a hybrid perturbation strategy is used to generate strong, diverse perturbations, and enhance unlabeled images information mining. The conventional CNN-based counting methods are sensitive to texture perturbation and imperceptible noises raised by adversarial attack, therefore, the hybrid strategy is proposed to combine a spatial texture transformation and an adversarial perturbation module to perturb the unlabeled data in the semantic and non-semantic spaces, respectively. Moreover, a cross-distribution normalization technique is introduced to address the model optimization failure caused by BN layer in the strong perturbation, and to stabilize the optimization of the learning model. Extensive experiments have been conducted on the datasets of ShanghaiTech, UCF-QNRF, NWPU-Crowd, and JHU-Crowd++. The results demonstrate that the proposed semi-supervised counting method performs better over the state-of-the-art methods, and it shows better robustness to various perturbations.