ICA-Net:improving class activation for weakly supervised semantic segmentation via joint contrastive and simulation learning  

作　　者：YE Zhuang LIU Ruyu SUN Bo 

机构地区：[1]School of Information Engineering,Chengdu Aviation Vocational and Technical College,Chengdu,610100,China [2]School of Computer Science and Engineering,Hangzhou Normal University,Hangzhou,311121,China [3]Quanzhou Institute of Equipment Manufacturing,Haixi Institutes,Chinese Academy of Sciences,Quanzhou,362200,China

出　　处：《Optoelectronics Letters》2025年第3期188-192,共5页光电子快报(英文版)

摘　　要：In the field of optoelectronics,certain types of data may be difficult to accurately annotate,such as high-resolution optoelectronic imaging or imaging in certain special spectral ranges.Weakly supervised learning can provide a more reliable approach in these situations.Current popular approaches mainly adopt the classification-based class activation maps(CAM)as initial pseudo labels to solve the task.

关 键 词：high resolution imaging supervised learning class activation maps joint contrastive simulation learning special spectral ranges weakly supervised learning OPTOELECTRONICS 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]