[1]WANG Kaicheng,LU Huaxiang,GONG Guoliang,et al.Salient object detection method based on the attention mechanism[J].CAAI Transactions on Intelligent Systems,2020,15(5):956-963.[doi:10.11992/tis.201903001]
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Salient object detection method based on the attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 956-963 Column: 学术论文—机器感知与模式识别 Public date: 2020-09-05
Title:
Salient object detection method based on the attention mechanism
Author(s):
WANG Kaicheng1; 2;  LU Huaxiang1; 3; 4;  GONG Guoliang1;  CHEN Gang1
1. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
2. School of Future Technology, University of Chinese Academy of Sciences, Beijing 100089, China;
3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China;
4. Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Lab, Beijing 100083, China
Keywords:
salient object detection; deep learning; fully convolutional neural network; visual attention; multi-scale features; image processing; artificial intelligence; computer vision
CLC:
TP391
DOI:
10.11992/tis.201903001
Abstract:
Salient object detection simulates human visual mechanism. At present, the mainstream methods are based on fully convolutional neural networks. Limited by the receptive fields of convolution layers, low-level features lack a global description of images, whereas high-level features are too coarse to accurately segment details of objects, such as edges, because of multi-stage downsampling operations. To solve this problem, we propose a salient object detection method based on the attention mechanism. We introduce novel attention refinement modules. The ground-truth attention calculated from the training datasets is employed to supervise spatial attention. Through this method, the network learns more accurate position relevance between different pixels. In addition, to refine the output salient maps, we gradually combine the multi-scale features and optimize low-layer features with high-layer features. Sufficient experiments on DUT-OMRON and ECSSD datasets have demonstrated that the proposed method outperforms the others in terms of the value of the F measure and mean absolute error.
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