Weakly Supervised Defect Detection

Weakly Supervised Defect Detection is a defect detection paradigm that belongs to the class of machine learning approaches utilizing incomplete, inexact, or imprecise supervision, distinguished by its reliance on limited or coarse-grained labels—such as image-level annotations instead of pixel-level annotations—to train models for identifying defects in industrial or material inspection tasks.

47

Publications

2.5K

Citations

198

Authors

66

Institutions

Publications per year

2017–2026

44

Authors

198

  1. XW
    Xinggang Wang

    Huazhong University of Science and Technology, China

    3 publications · 94 citations

    3

    h-index

  2. KS
    Kechen Song

    Northeastern University, China

    3 publications · 248 citations

    3

    h-index

  3. YC
    Yanlong Cao

    Zhejiang University, China

    2 publications · 69 citations

    2

    h-index

  4. YC
    Yanpeng Cao

    Zhejiang University, China

    3 publications · 109 citations

    2

    h-index

  5. SN
    Shuanlong Niu

    Huazhong University of Science and Technology, China

    2 publications · 81 citations

    2

    h-index

1–5 of 198

Venues

  1. 3

    h-index

  2. Journal of Intelligent Manufacturing

    2 publications · 60 citations

    2

    h-index

  3. Computerized Medical Imaging and Graphics

    CMIG · 2 publications · 168 citations

    2

    h-index

  4. Pattern Recognition

    2 publications · 126 citations

    2

    h-index

  5. Measurement

    2 publications · 106 citations

    2

    h-index

1–5