计算机应用与软件2024，Vol.41Issue(5)：166-170,196,6.DOI:10.3969/j.issn.1000-386x.2024.05.026
基于特征残差融合的显著性检测网络
NETWORK FOR SALIENCY DETECTION BASED ON RESIDUAL FUSION OF FEATURES
徐玉菁 1李洪鹏1
作者信息
- 1. 东南大学成贤学院 江苏南京 210000
- 折叠
摘要
Abstract
Benefitting from convolution neural network with supervised training,recent works of saliency detection achieves good results.However,it is still a core issue that how to effectively use the salient features in the model.We believe that the fusion of different levels of saliency feature information can complement each other and promote effect of the final prediction.In this paper,a network framework based on local information residual fusion is proposed.This framework was to fuse the features of the local convolution layer in the form of residual error,so as to avoid the risk of introducing noise due to too many sampling operations.The fused new feature map was transmitted from deep layer to shallow layer progressively,and the final prediction result was obtained.关键词
显著性目标检测/残差结构/深度学习/计算机视觉Key words
Significance target detection/Residual structure/Deep learning/Computer vision分类
信息技术与安全科学引用本文复制引用
徐玉菁,李洪鹏..基于特征残差融合的显著性检测网络[J].计算机应用与软件,2024,41(5):166-170,196,6.
