Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation

18 March 2020

Papers citing "Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation"

4 / 4 papers shown

Title
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection Yun Peng Xiao Lin Nachuan Ma Jiayuan Du Chuangwei Liu Chengju Liu Qi Chen 101 3 0 17 Feb 2025
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes Yuanpeng Tu Yuxi Li Boshen Zhang Liang Liu Jing Zhang Yun Wang C. Zhao 74 3 0 03 Jan 2025
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection Tingfeng Huang Yuxuan Cheng Jingbo Xia Rui Yu Yuxuan Cai Jinhai Xiang Xinwei He AAML 86 0 0 14 Aug 2024
A large annotated medical image dataset for the development and evaluation of segmentation algorithms Amber L. Simpson Michela Antonelli Spyridon Bakas Michel Bilello Keyvan Farahani ... M. McHugo S. Napel Eugene Vorontsov Lena Maier-Hein M. Jorge Cardoso 47 846 0 25 Feb 2019