Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting
Single-source domain generalization for crowd counting is highly challenging because a single labeled source domain may contain heterogeneous latent domains, while unseen target domains often exhibit severe distribution shifts. A central issue is stable latent domain discovery: directly performing flat clustering on evolving sample-level latent features is easily disturbed by feature noise, outliers, and representation drift, leading to unreliable pseudo-domain assignments and weakened domain-structured learning. To address this problem, we propose a granular ball guided stable latent domain discovery framework for domain-general crowd counting. The proposed method first groups samples in...
| Authors | Fan Chen, Shuyin Xia, Yi Wang |
|---|---|
| Primary category | cs.CV |
| Published | 25 March 2026 |
| Last updated at source | 31 March 2026 |
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