Find Gold in Sand: Fine-Grained Similarity Mining for Domain-Adaptive Crowd Counting

The domain shift of crowd scenes significantly hinders the application of crowd counting models in open scenarios. Although domain adaptation methods for crowd counting have bridged this gap to some extent, they ignore one of the significant causes of domain shift, which is the inter-domain data distribution bias. We discover that there exists a connection between the known and unknown distribution, which can be utilized by similarity mining to address the domain shift. However, there are still challenges related to insufficient and inaccurate similarity mining. In this article, a novel Fine-grained Inter-domain Similarity Mining (FSIM) framework is proposed. To comprehensively explore the similar distributions between source and target domains, we propose a Multi-scale Distribution Alignment (MDA) module based on diffusion retrieval. To enhance the reliability of inter- domain similarity mining, we propose a Multi-retrieval Refinement (MR) module based on evidence theory, which serves as an uncertainty measurement method. Eventually, to eliminate the data distribution bias, we perform model retraining using a similar distribution. Extensive experiments conducted on five standard crowd counting benchmarks, SHA, SHB, QNRF, NWPU, and JHU-CROWD++, show that the proposed FSIM has strong generalizability.