Co-salient object detection with iterative purification and predictive optimization  

作　　者：Yang WEN Yuhuan WANG Hao WANG Wuzhen SHI Wenming CAO 

机构地区：[1]Guangdong Provincial Key Laboratory of Intelligent Information Processing,College of Electronics and Information Engineering,Shenzhen University,Shenzhen 518060,China

出　　处：《虚拟现实与智能硬件（中英文）》2024年第5期396-407,共12页Virtual Reality & Intelligent Hardware

基　　金：Supported by the National Natural Science Foundation of China under Grant(62301330,62101346);the Guangdong Basic and Applied Basic Research Foundation(2024A1515010496,2022A1515110101);the Stable Support Plan for Shenzhen Higher Education Institutions(20231121103807001);the Guangdong Provincial Key Laboratory under(2023B1212060076).

摘　　要：Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related images.However,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the co-representation.These issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of detection.Methods To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD algorithm.Furthermore,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing benchmarks.Experiments on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.

关 键 词：Co-salient object detection Saliency detection Iterative method Predictive optimization 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]