Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting

• URL: http://arxiv.org/abs/2603.24106v2
• Date: Tue, 31 Mar 2026 08:45:00 GMT
• Title: Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting
• Abstract summary: We propose a granular ball guided stable latent domain discovery framework for domain-general crowd counting.<n>The proposed method first groups samples into compact local granular balls and then clusters granular ball centers as representatives to infer pseudo-domains.<n>On top of the discovered latent domains, we develop a two-branch learning framework that improves transferable semantic representations.
• Score: 19.18297173252027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 into compact local granular balls and then clusters granular ball centers as representatives to infer pseudo-domains, thereby converting direct sample-level clustering into a hierarchical representative-based clustering process. This design produces more stable and semantically consistent pseudo-domain assignments. On top of the discovered latent domains, we develop a two-branch learning framework that improves transferable semantic representations via semantic codebook re-encoding and captures domain-specific appearance variations through a style branch, thereby alleviating semantic--style entanglement under domain shifts. Extensive experiments on ShanghaiTech A/B, UCF\_QNRF, and NWPU-Crowd under a strict no-adaptation protocol verify the effectiveness of the proposed method and show strong generalization ability, especially in transfer settings with large domain gaps.

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