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weakly supervised object localization with Background Suppression Erasing for Art Authentication and Copyright Protection

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Machine Intelligence Research 2024年第1期21卷 89-103页

作者： Chaojie Wu Mingyang Li Ying Gao Xinyan Xie Wing W.Y.Ng Ahmad Musyafa School of Computer Science and Engineering South China University of TechnologyGuangzhou 511442China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's HospitalGuangzhou 510180China

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 *** art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright ***,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level *** alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level ***,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic ***,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance ***,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization *** experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE.

关键词： weakly supervised object localization erasing method deep learning computer vision art authentication and copyright protection

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Shallow Feature-driven Dual-edges localization Network for weakly supervised localization

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Machine Intelligence Research 2023年第6期20卷 923-936页

作者： Wenjun Hui Guanghua Gu Bo Wang School of Information Science and Engineering Yanshan UniversityQinhuangdao066000China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066000China

weakly supervised object localization mines the pixel-level location information based on image-level *** traditional weakly supervised object localization approaches exploit the last convolutional feature map to locate the discriminative regions with abundant *** it shows the localization ability of classification network,the process lacks the use of shallow edge and texture features,which cannot meet the requirement of object integrity in the localization ***,we propose a novel shallow feature-driven dual-edges localization(DEL)network,in which dual kinds of shallow edges are utilized to mine entire target object ***,we design an edge feature mining(EFM)module to extract the shallow edge details through the similarity measurement between the original class activation map and shallow *** exploit the EFM module to extract two kinds of edges,named the edge of the shallow feature map and the edge of shallow gradients,for enhancing the edge details of the target object in the last convolutional feature *** total process is proposed during the inference stage,which does not bring extra training *** experiments on both the ILSVRC and CUB-200-2011 datasets show that the DEL method obtains consistency and substantial performance improvements compared with the existing methods.

关键词： weakly supervised object localization edge feature mining edge of shallow feature map edge of shallow gradients similarity measurement

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