Domain-Agnostic Crowd Counting via Uncertainty-Guided Style Diversity Augmentation

Domain shift significantly hinders crowd counting performance in unseen domains. Domain adaptation methods tackle this issue using target domain images but falter when acquiring these images is difficult. Moreover, they demand additional training time for fine-tuning. To address this issue, we propose an Uncertainty-Guided Style Diversity Augmentation (UGSDA) method, enabling the models to be trained solely on the source domain and directly generalized to various target domains. It is achieved by generating sufficiently diverse and realistic samples during the training process. Specifically, our UGSDA method incorporates three tailor-designed components: the Global Styling Elements Extraction (GSEE) module, the Local Uncertainty Perturbations (LUP) module, and the Density Distribution Consistency (DDC) loss. The GSEE extracts global style elements from the feature space of the whole source domain. The LUP aims to obtain uncertainty perturbations from the batch-level input to form style distributions beyond the source domain, which used to generate diversified stylized samples together with global style elements. To regulate the extent of perturbations, the DDC loss imposes constraints between the source samples and the stylized samples, ensuring the stylized samples maintain a higher degree of realism and reliability. Comprehensive experiments validate the superiority of our approach, demonstrating its strong generalization capabilities across various datasets and models. Code is available at https://github.com/gcding/UGSDA-pytorch.