Attention Modules Improve Modern Image-Level Anomaly Detection: A DifferNet Case Study
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco（伯南布哥联邦大学信息中心Voxar实验室）；Visual Computing Lab, DC, Universidade Federal Rural de Pernambuco（伯南布哥联邦农村大学视觉计算实验室）；Junior Professorship of Media Computing, Chemnitz University of Technology（开姆尼茨工业大学媒体计算初级教授职位）
André Luiz B. Vieira e Silva, Francisco Simões, Danny Kowerko, Tobias Schlosser, Felipe Battisti, Veronica Teichrieb
备注：
Accepted to CVPRW 2023: VISION'23 - 1st workshop on Vision-based InduStrial InspectiON (Extended Abstract). arXiv admin note: substantial text overlap with arXiv:2311.02747
Within (semi-)automated visual inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To not only alleviate this issue but to furthermore advance the current state of the art in unsupervised visual inspection, this contribution proposes a DifferNet-based solution enhanced with attention modules utilizing SENet and CBAM as backbone - AttentDifferNet - to improve the detection and classification capabilities on three different visual inspection and anomaly detection datasets: MVTec AD, InsPLAD-fault, and Semiconductor Wafer. In comparison to the current state of the art, it is shown that AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quantitative as well as qualitative evaluation, indicated by a general improvement in AUC of 94.34 vs. 92.46, 96.67 vs. 94.69, and 90.20 vs. 88.74%. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for anomaly detection.
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco（佩南布科联邦大学信息学中心 Voxar 实验室）；Visual Computing Lab, DC, Universidade Federal Rural de Pernambuco（佩南布科联邦农村大学 DC 视觉计算实验室）；Chemnitz University of Technology（开姆尼茨工业大学）
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold
Digital Sense（数字感知公司）；Facultad de Ingeniería, Universidad de la República（乌拉圭共和国大学工程学院）；Departamento de Ingeniería, Universidad Católica del Uruguay（乌拉圭天主教大学工程系）
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
Fudan University（复旦大学）；Youtu Lab, Tencent（腾讯优图实验室）；Shanghai Jiao Tong University（上海交通大学）；Rongcheer Co., Ltd（融雪科技有限公司）；City University of Hong Kong（香港城市大学）；National University of Singapore（新加坡国立大学）；Shanghai Ocean University（上海海洋大学）
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；Inception Institute of AI (IIAI)（Inception人工智能研究院（IIAI））
VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics
Forensics and Security Research Group, South East Technological University（法医与安全研究组，南东技术大学）；Security Research Group, School of Computer Science, University College Dublin（安全研究组，计算机科学学院，都柏林大学学院）
