{
  "annotation_id": "ad82dd57-37f7-41e0-8ed7-cf939ed9f8e4",
  "date_created": "2026-02-17T05:53:20.057000Z",
  "date_modified": "2026-02-17T05:53:20.057000Z",
  "file_hash": "28ccb62338a66fc157d549b9171526d2edbc9c2a865ec8c707a806fdc4ffc4ea",
  "private": false,
  "record": {
    "abstract": "Single-domain generalized deep metric learning (SDG-DML) faces the dual challenge of both category and domain shifts during testing, limiting real-world applications. Therefore, aiming to learn better generalization ability on both unseen categories and domains is a realistic goal for the SDG-DML task. To deliver the aspiration, existing SDG-DML methods employ the domain expansion-equalization strategy to expand the source data and generate out-of-distribution samples. However, these methods rely on proxy-based expansion, which tends to generate samples clustered near class proxies, failing to simulate the broad and distant domain shifts encountered in practice. To alleviate the problem, we propose CenterPolar, a novel SDG-DML framework that dynamically expands and constrains domain distributions to learn a generalizable DML model for wider target domain distributions. Specifically, \\textbf{CenterPolar} contains two collaborative class-centric polarization phases: (1) Class-Centric Centrifugal Expansion ($C^3E$) and (2) Class-Centric Centripetal Constraint ($C^4$). In the first phase, $C^3E$ drives the source domain distribution by shifting the source data away from class centroids using centrifugal expansion to generalize to more unseen domains. In the second phase, to consolidate domain-invariant class information for the generalization ability to unseen categories, $C^4$ pulls all seen and unseen samples toward their class centroids while enforcing inter-class separation via centripetal constraint. Extensive experimental results on widely used CUB-200-2011 Ext., Cars196 Ext., DomainNet, PACS, and Office-Home datasets demonstrate the superiority and effectiveness of our CenterPolar over existing state-of-the-art methods. The code will be released after acceptance.",
    "arxiv_id": "2601.09121",
    "authors": [
      "Xin Yuan",
      "Meiqi Wan",
      "Wei Liu",
      "Xin Xu",
      "Zheng Wang"
    ],
    "categories": [
      "cs.CV"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Beyond Seen Bounds: Class-Centric Polarization for Single-Domain Generalized Deep Metric Learning",
    "url": "https://arxiv.org/abs/2601.09121",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0e664928-5e91-4720-a9b7-797acdc365e2",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}