显著性引导的目标互补隐藏弱监督语义分割  

Saliency guided object complementary hiding for weakly supervised semantic segmentation

作　　者：白雪飞[1] 卢立彬 王文剑[1,2] Bai Xuefei;Lu Libin;Wang Wenjian(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University),Ministry of Education,Taiyuan 030006,China)

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]计算智能与中文信息处理教育部重点实验室(山西大学),太原030006

出　　处：《中国图象图形学报》2024年第4期1041-1055,共15页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61703252,U21A20513,62076154,62276161);山西省重点研发项目(202102150401013);山西省回国留学人员科研资助项目(2022-008)。

摘　　要：目的 图像级弱监督语义分割方法利用类别标签训练分割网络,可显著降低标注成本。现有方法大多采用类激活图定位目标物体,然而传统类激活图只能挖掘出物体中最具辨识性的区域,直接将其作为伪标签训练的分割网络精度较差。本文提出一种显著性引导的弱监督语义分割算法,可在获取更完整类激活图的基础上提高分割模型的性能。方法 首先通过显著图对目标进行互补随机隐藏,以获得互补图像对,然后融合互补图像对的类激活图作为监督,提高网络获取完整类激活图的能力。其次引入双重注意力修正模块,利用全局信息修正类激活图并生成伪标签训练分割网络。最后使用标签迭代精调策略,结合分割网络的初始预测、类激活图以及显著图生成更精确的伪标签,迭代训练分割网络。结果 在PASCAL VOC 2012(pattern analysis,statistical modeling and computational learning visual object classes 2012)数据集上进行类激活图生成实验与语义分割实验,所生成的类激活图更加完整,平均交并比有10.21%的提升。语义分割结果均优于对比方法,平均交并比提升6.9%。此外在COCO 2014(common object in context 2014)数据集上进行了多目标的语义分割实验,平均交并比提升0.5%。结论 该算法可获得更完整的类激活图,缓解了弱监督语义分割中监督信息不足的问题,提升了弱监督语义分割模型的精度。Objective The fully supervised semantic segmentation method based on deep learning has made remarkable progress,promoting practical applications such as automatic driving and medical image analysis.However,the fully supervised semantic segmentation method depends on the complete pixel-wise annotation,and the construction of large-scale pixel-wise annotation datasets requires a considerable amount of human labor and resources.Researchers have recently attempted to study semantic segmentation based on convenient supervisions,such as bounding boxes,scribbles,points,and image-level labels,to reduce the reliance on accurate annotations.Weakly supervised semantic segmentation based on image-level labels only uses category labels to train the segmentation network,which can significantly reduce the annotation cost.Most of the existing weakly supervised semantic segmentation methods use class activation map(CAM)to locate target objects.On the one hand,the CAM generated by classification networks is sparse and can only focus on the most discriminative areas of objects.Some misactivated pixels are observed in the CAM,which may provide improper guidance for the subsequent segmentation task.On the other hand,the performance of the segmentation network depends on the quality of the pseudo labels.Thus,obtaining the accurate pseudo label also requires the shape and boundary of the object.However,this information cannot be directly and accurately obtained in image-level labels,and guaranteeing the quality of pseudo labels is difficult.A new saliency-guided weakly supervised semantic segmentation algorithm is proposed in this paper to improve the performance of the segmentation model to obtain complete CAMs.Method First,research shows that randomly hiding the target in the image can enhance the capability of the network to locate the complete target.However,part of the image information cannot be used when directly hiding the image at random.By contrast,the complementary hiding method can use all the image information.However,guar

关 键 词：深度学习 弱监督语义分割 显著性引导 类激活图(CAM) 注意力机制 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]