文献类型：期刊文献

英文题名：Global Context Guided Multi-scale Feature Network for Salient Object Detection

作者：Zhao, Zhenyu[1]; Fang, Yachao[1]; Zhang, Qing[1]; Chen, Xiaowei[1]; Dai, Meng[1]; Lin, Jiajun[2]

机构：[1] Shanghai Institute of Technology, Shanghai, 201418, China; [2] East China University of Science and Technology, Shanghai, 200237, China

年份：2022

卷号：801 LNEE

起止页码：81

外文期刊名：Lecture Notes in Electrical Engineering

收录：EI(收录号：20214411093819)

语种：英文

外文关键词：Object recognition - Convolution - Feature extraction

摘要：Currently, fully convolutional network based salient object detection approaches have some challenging problems. This paper proposes a novel salient object detection approach using global context and multi-scale feature representation to estimate saliency maps in a pixel-wise manner. Firstly, we explore and design a multi-scale feature enhancement module to improve the capability of feature representation and learning of multi-level side-output features. Moreover, we use global features to guide side-output multi-scale features to focus on the useful information, which could help the network effectively locate salient objects and suppress background noises. Finally, the feature pyramid network structure is utilized to refine the estimated results in a coarse-to-fine manner, and then obtain the final predicted results. The comparisons of our approach and 15 state-of-the-art methods demonstrate the effictiveness and robustness of the proposed approach on various scenarios. ? 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.