In this paper, we present an automated pipeline for generating domain-specific synthetic datasets with diffusion models, addressing the distribution shift between pre-trained models and real-world deployment environments. Our three-stage framework first synthesizes target objects within domain-specific backgrounds through controlled inpainting. The generated outputs are then validated via a multi-modal assessment that integrates object detection, aesthetic scoring, and vision-language alignment. Finally, a user-preference classifier is employed to capture subjective selection criteria. This pipeline enables the efficient construction of high-quality, deployable datasets while reducing reliance on extensive real-world data collection.
Mind the Gaps: A Curated Benchmark for Form Field Detection
Luleå University of Technology（吕勒奥理工大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；LATIS-Laboratory of Advanced Technology and Intelligent Systems, National Engineering School of Sousse (ENISo), University of Sousse（LATIS先进技术与智能系统实验室，苏斯国家工程学院（ENISo），苏斯大学）
Department of Gastroenterology, Hospital Santa Luzia, ULS Alto Minho, Viana do Castelo, Portugal（圣卢西亚医院消化内科部，葡萄牙）；Center Algoritmi / LASI, University of Minho, Braga, 4710-057, Portugal（米尼奥大学算法中心/拉斯伊，葡萄牙）；Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), University of Duisburg-Essen, Essen, Germany（医学人工智能研究所（IKIM），埃森大学医院（AöR），德国）；Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal（生命与健康科学研究所（ICVS），米尼奥大学医学院，葡萄牙）；ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães（ICVS/3B’s - 葡萄牙政府关联实验室，布拉加/吉马拉雷斯）
SpurBreast: A Curated Dataset for Investigating Spurious Correlations in Real-world Breast MRI Classification
Center for Biosystems；Biotech Data Science, Ghent University Global Campus, Incheon, Republic of Korea IDLab, ELIS, Ghent University, Ghent, Belgium Department of Applied Mathematics, Computer Science；Statistics, Ghent University, Ghent, Belgium ( )
