文献类型：期刊文献

英文题名：Attention and boundary guided salient object detection

作者：Zhang, Qing[1];Shi, Yanjiao[1];Zhang, Xueqin[2]

机构：[1]Shanghai Inst Technol, Sch Comp Sci & Informat Engn, Shanghai 201418, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：107

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20202508839276);WOS:【SCI-EXPANDED(收录号:WOS:000552866000034)】；

基金：This work is supported by National Science Foundation of Shanghai under Grant No. 19ZR1455300, Science and Technology Development Foundation of Shanghai Institute of Technology under Grant No.ZQ2018-23, and National Natural Science Foundation of China under Grant No. 61806126

语种：英文

外文关键词：Salient object detection; Visual saliency; Feature learning; Fully convolutional neural network

摘要：In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images. There is still a large room for improvement over the FCN-based saliency detection approaches. In this paper, we propose an attention and boundary guided deep neural network for salient object detection to better locate and segment the salient objects with uniform interior and explicit boundary. A channel-wise attention module is utilized to emphasize the important regions, which selects the important feature channels and assigns large weights to them. A boundary information localization module is proposed for suppressing the irrelevant boundary information to better locate and explore the useful structure of objects. The proposed approach achieves state-of-the-art performance on four well-known benchmark datasets. (C) 2020 Elsevier Ltd. All rights reserved.