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cs.CV2025

ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detection

Qunyi Zhang, Songan Zhang, Jiaqi Liu +5

Anomaly detection plays a pivotal role in manufacturing quality control, yet its application is constrained by limited abnormal samples and high manual annotation costs. While anom…

cs.CV2025

STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment

Xichen Xu, Yanshu Wang, Jinbao Wang +5

Segmentation-oriented Industrial Anomaly Synthesis (SIAS) plays a pivotal role in enhancing the performance of downstream anomaly segmentation, as it provides an effective means of…

cs.CV2025

FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis

Xichen Xu, Yanshu Wang, Jinbao Wang +4

Industrial anomaly segmentation relies heavily on pixel-level annotations, yet real-world anomalies are often scarce, diverse, and costly to label. Segmentation-oriented industrial…

cs.CV2025

SARD: Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance

Yanshu Wang, Xichen Xu, Xiaoning Lei +1

Synthesizing realistic and spatially precise anomalies is essential for enhancing the robustness of industrial anomaly detection systems. While recent diffusion-based methods have…

cs.CV2025

A Survey on Industrial Anomalies Synthesis

Yanshu Wang, Xichen Xu, Jiaqi Liu +4

This paper comprehensively reviews anomaly synthesis methodologies. Existing surveys focus on limited techniques, missing an overall field view and understanding method interconnec…