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

• URL: http://arxiv.org/abs/2603.24106v1
• Date: Wed, 25 Mar 2026 09:12:35 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>Specifically, the proposed method first organizes samples into compact local granular balls and then clusters granular ball centers as representatives to obtain pseudo-domains.<n>This design yields more stable and semantically consistent pseudo-domain assignments.
• Score: 59.55838360454893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-source domain generalization for crowd counting remains highly challenging because a single labeled source domain often contains heterogeneous latent domains, while test data may exhibit severe distribution shifts. A fundamental difficulty lies in stable latent domain discovery: directly performing flat clustering on evolving sample-level latent features is easily affected by feature noise, outliers, and representation drift, leading to unreliable pseudo-domain assignments and weakened domain-structured learning. To address this issue, we propose a granular ball guided stable latent domain discovery framework for domain-general crowd counting. Specifically, the proposed method first organizes samples into compact local granular balls and then clusters granular ball centers as representatives to obtain pseudo-domains, transforming direct sample-level clustering into a hierarchical representative-based clustering process. This design yields more stable and semantically consistent pseudo-domain assignments. Built upon the discovered latent domains, we further develop a two-branch learning framework that enhances transferable semantic representations via semantic codebook re-encoding while modeling domain-specific appearance variations through a style branch, thereby reducing semantic--style entanglement and improving generalization under domain shifts. Extensive experiments on ShanghaiTech A/B, UCF\_QNRF, and NWPU-Crowd under a strict no-adaptation protocol demonstrate that the proposed method consistently outperforms strong baselines, especially under large domain gaps.

Related papers

• Domain-invariant Mixed-domain Semi-supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment [11.298724831730675]
  We propose a domain-invariant mixed-domain semi-supervised segmentation framework.<n>A Copy-Paste Mechanism (CPM) augments the training set by transferring informative regions across domains.<n>A Cluster Maximum Mean Discrepancy (CMMD) block clusters unlabeled features and aligns them with labeled anchors.
  arXiv  Detail & Related papers  (2026-01-23T18:23:03Z)
• ConstStyle: Robust Domain Generalization with Unified Style Transformation [16.853951836297203]
  Domain Generalization (DG) aims to address this by focusing on domain-invariant features or augmenting data for greater diversity.<n>We propose ConstStyle, a novel approach that leverages a unified domain to capture domain-invariant features and bridge the domain gap with theoretical analysis.
  arXiv  Detail & Related papers  (2025-09-07T08:40:19Z)
• What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization [10.079844840768054]
  Domain Generalization aims to develop models that can generalize to novel and unseen data distributions.<n>We study how model architectures and pre-training objectives impact feature richness.<n>Our framework improves generalization to unseen domains by a maximum test accuracy improvement of over 4%.
  arXiv  Detail & Related papers  (2025-03-09T17:29:01Z)
• Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization [70.02187124865627]
  Open-set single-source domain generalization aims to use a single-source domain to learn a robust model that can be generalized to unknown target domains. We propose a novel learning approach based on domain expansion and boundary growth to expand the scarce source samples. Our approach can achieve significant improvements and reach state-of-the-art performance on several cross-domain image classification datasets.
  arXiv  Detail & Related papers  (2024-11-05T09:08:46Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing [53.82826073959756]
  Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. We propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels. To overcome the limitation, we propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels.
  arXiv  Detail & Related papers  (2021-05-06T06:04:59Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Bi-Directional Generation for Unsupervised Domain Adaptation [61.73001005378002]
  Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. Conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. We propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
  arXiv  Detail & Related papers  (2020-02-12T09:45:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.