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Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples
  Through Normal Background Regularization and Crop-and-Paste Operation
v1v2 (latest)

Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation

18 July 2020
Dongyun Lin
Yanpeng Cao
Wenbin Zhu
Yiqun Li
    UQCV
ArXiv (abs)PDFHTML

Papers citing "Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation"

3 / 3 papers shown
AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer
AnoStyler: Text-Driven Localized Anomaly Generation via Lightweight Style Transfer
Yulim So
Seokho Kang
DiffM
160
0
0
10 Nov 2025
A Novel Strategy for Improving Robustness in Computer Vision
  Manufacturing Defect Detection
A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection
A. M. Mezher
A. Marble
UQCVAAMLOOD
192
6
0
16 May 2023
Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation
Few-Shot Defect Image Generation via Defect-Aware Feature ManipulationAAAI Conference on Artificial Intelligence (AAAI), 2023
Yuxuan Duan
Y. Hong
Li Niu
Liqing Zhang
333
131
0
04 Mar 2023
1
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