Few-shot Online Anomaly Detection and Segmentation

Few-shot Online Anomaly Detection and Segmentation

27 March 2024

Papers citing "Few-shot Online Anomaly Detection and Segmentation"

9 / 9 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 37 3 0 17 Feb 2025
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring Eirini Cholopoulou D. Iakovidis AAML 16 0 0 20 Sep 2024
AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection Mykhailo Koshil Tilman Wegener Detlef Mentrup Simone Frintrop Christian Wilms 27 1 0 27 Aug 2024
Dual-path Frequency Discriminators for Few-shot Anomaly Detection Yuhu Bai Jiangning Zhang Yuhang Dong Guanzhong Tian Liang Liu Yunkang Cao Yabiao Wang Chengjie Wang 27 2 0 07 Mar 2024
Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection Yufei Liang Jiangning Zhang Shiwei Zhao Ru-Chwen Wu Yong-Jin Liu Shuwen Pan 118 114 0 01 Mar 2022
Frustratingly Simple Few-Shot Object Detection Xin Wang Thomas E. Huang Trevor Darrell Joseph E. Gonzalez F. I. F. Richard Yu ObjD 75 535 0 16 Mar 2020
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning Xiaopeng Yan Ziliang Chen Anni Xu Xiaoxi Wang Xiaodan Liang Liang Lin ObjD 151 440 0 28 Sep 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,568 0 09 Mar 2017
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks Subarna Tripathi Zachary Chase Lipton Serge J. Belongie Truong Thao Nguyen ObjD 72 63 0 15 Jul 2016