首页|Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

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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.

Weakly supervised object localizationerasing methoddeep learningcomputer visionart authentication and copyright protection

Chaojie Wu、Mingyang Li、Ying Gao、Xinyan Xie、Wing W.Y.Ng、Ahmad Musyafa

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School of Computer Science and Engineering,South China University of Technology,Guangzhou 511442,China

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application,Guangdong Provincial People's Hospital,Guangzhou 510180,China

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application,China

2022B1212010011

2024

10.1007/s11633-023-1455-3

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(1)
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