{
  "abstract": "Few-shot strip-steel surface defect segmentation requires a model to identify unseen defect categories and delineate their precise boundaries from only a few annotated support images. This remains challenging because strip-steel defects are often small, elongated, low-contrast, and easily confused with repetitive background textures. To address these challenges, we propose the Support-Driven Spatial–Semantic Decoupling Network (S3DNet), which preserves a frozen DINOv3 encoder while explicitly separating support-conditioned semantic adaptation from fine-grained spatial refinement. Specifically, a low-rank prototype adapter transforms the masked support prototype into an episode-conditioned semantic update of the query tokens, enabling the frozen representation to adapt to the defect category specified by the current support set. In parallel, a lightweight spatial adapter extracts multiscale local cues for recovering weak boundaries and thin structures. A spatial–semantic interaction module subsequently uses support–query semantic similarity to selectively gate these spatial cues before progressive decoding, thereby suppressing irrelevant texture responses while retaining defect-relevant details. Experiments on FSSD-12 demonstrate that S3DNet consistently outperforms representative CNN-based few-shot segmentation methods under both 1-shot and 5-shot settings, while the lightweight DINOv3-Small configuration achieves a favorable accuracy–efficiency trade-off. Ablation and qualitative analyses further validate the effectiveness and complementarity of the proposed semantic adaptation, spatial refinement, and interaction mechanisms.",
  "abstract_shown": true,
  "affiliations": [
    "Gezhouba Hydropower Station, China Yangtze Power Co., Ltd., Yichang 443002, China",
    "Zhejiang Dahua Technology Co., Ltd., Hangzhou 310000, China",
    "School of Automation, Hangzhou Dianzi University, Hangzhou 310000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ju Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Jingguang Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Fengling Lei"
    },
    {
      "affs": [
        0
      ],
      "name": "Baitong Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhiguo Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Jiacheng Huang"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaofei Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/52tetryqdeh13.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203730",
  "fulltext_url": null,
  "id": "52tetryqdeh13",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3730",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2209|Industrial Vision Systems and Defect Detection"
    ]
  ],
  "openalex": "W7221776461",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/52tetryqdeh13.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203730",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3730"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "S3DNet: Support-Driven Spatial–Semantic Decoupling for Few-Shot Strip-Steel Surface Defect Segmentation",
  "title_text": "S3DNet: Support-Driven Spatial–Semantic Decoupling for Few-Shot Strip-Steel Surface Defect Segmentation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/52tetryqdeh13",
  "venue": "Mathematics",
  "versions": []
}