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| 1 | Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection显示文摘The problem of art forgery and infringement is becoming increasingly prominent,since diverse self-media contents with all kinds of art pieces are released on the Internet every day.For art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright protection.However,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level annotations.To alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level labels.First,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic information.Second,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance mechanism.Finally,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization results.Extensive experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE. | Chaojie Wu Mingyang Li Ying Gao Xinyan Xie Wing W.Y.Ng Ahmad Musyafa | 2024 | Machine Intelligence Research2024，21，1： | 0 |
| 2 | Deep Industrial Image Anomaly Detection: A Survey显示文摘The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection(IAD).In this pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and datasets.In addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed setting.Moreover,we highlight several opening chal-lenges for image anomaly detection.The merits and downsides of representative network architectures under varying supervision are discussed.Finally,we summarize the research findings and point out future research directions.More resources are available at http://gffzz188fe103f8f1460as90w9966q9woo6nv0.ffgz.tsg.suse.edu.cn/M-3LAB/awesome-industrial-anomaly-detection. | Jiaqi Liu Guoyang Xie Jinbao Wang Shangnian Li Chengjie Wang Feng Zheng Yaochu Jin | 2024 | Machine Intelligence Research2024，21，1： | 0 |
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