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

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.

Figures (4)

• Figure 1: Illustration of three settings under domain shift: fully supervised learning, domain adaptation, and domain generalization.
• Figure 2: Overall framework of the proposed method, consisting of semantic codebook re-encoding, style-branch regularization, and granular ball guided stable latent domain discovery for robust density regression.
• Figure 3: Hierarchical division and merging of a weighted granular ball set (WGBS) for pseudo-domain discovery. Starting from the initial ball set $S^{(0)}$, parent balls are iteratively split by weighted 2-means and accepted only when the child compactness decreases sufficiently. After no further split is accepted, $K$-means is performed on granular ball centers to obtain pseudo-domains.
• Figure 4: Qualitative comparison on UCF_QNRF under the no-adaptation setting. From left to right: input image, GT density map, and the predictions of Ours, DGCC, UGSDA. The numbers denote crowd counts obtained by integrating the density maps.