no code implementations • 29 Jan 2024 • Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, Weiming Shen
Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e. g., industrial defect inspection, and medical lesion detection.
1 code implementation • 15 Jun 2023 • Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Liang Gao, Weiming Shen
This technical report introduces the winning solution of the team Segment Any Anomaly for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge.
2 code implementations • 18 May 2023 • Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Zongwei Du, Liang Gao, Weiming Shen
We present a novel framework, i. e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models.
Ranked #1 on Anomaly Detection on KSDD2