EGNet: Edge Guidance Network for Salient Object Detection

Jia-Xing Zhao,  Jiang-Jiang Liu,  Deng-Ping Fan,  Yang Cao,   Jufeng Yang,  Ming-Ming Cheng

TKLNDST, CS, Nankai University

Abstract

Fully convolutiona lneural networks(FCNs )have shown their advantages in the salient object detection task. However,most existing FCNs-based methods still suffer from the coarse object boundaries. In this paper, to solve this problem, we focus on the complementarity between the salient edge information and the salient object information. Accordingly, we present an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these complementary information in a single network. In the first step, we extract the salient object features by a progressive fusion way. In the second step, we integrate the local edge information and global location information to obtain the salient edge features. Finally, in order to suffciently leverage these complementary features, we couple the same salient edge features with salient object features at various resolutions. Benefiting from the rich edge information and location information in salient edge features, the fused features can help locate salient objects, especially their boundaries more accurately. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on six widely used datasets without any pre-processing and postprocessing.

Paper

• EGNet: Edge Guidance Network for Salient Object Detection, Jiaxing Zhao, Jiangjiang Liu, Dengping Fan, Yang Cao, Jufeng Yang, Ming-Ming Cheng, ICCV, 2019. [pdf][code and evaluation results]

Source Code

We release  the source code and provide all the evaluation results(SOD, ECSSD, PASCALS, DUT-OMRON, DUTS, HKU-IS, SOC) in the code page.

Most related projects on this website

• Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground, Deng-Ping Fan,  Ming-Ming Cheng,  Jiang-Jiang Liu, Shang-Hua Gao, Qibin Hou, Ali Borji. ECCV,2018. Accept rate: 29.4% [717/2439] [project page | latex | official version | 中文pdf] | evaluate code | Dataset  (730.2MB)  Baidu] [Google
• Structure-measure: A New Way to Evaluate Foreground Maps, Deng-Ping Fan, Yun Liu, TaoLi, Ming-Ming Cheng, Aliborji. IEEE ICCV, 2017. Spotlight presentation, Accept rate: 2.61% [56/2143] [official version | 中文版pdf | ICCV slides | Youtube| Matlab code | C++ code]

Method

Pipeline

The pipeline of our method. We use brown thick lines to represent information flows between the scales. PSFEM: progressive salient object features extraction module. NLSEM: non-local salient edge features extraction module. O2OGM : one-to-one guidance module. FF: feature fusion. Spv.: supervision. First, we explicitly model the edge information and get salient edge features . Then we leverage the salient edge features to guide the saliency features locate and segment the salient object better.

Qualitative comparisons

We compare our EGNet and the other state-of-the-art methods.

Quantitative comparisons

As we can see, our method performs favorably against the state-of-the-art methods on six widely used datasets without any pre-processing and post-processing.

If you find our work is helpful, please cite

@inproceedings{zhao2019EGNet,
 title={EGNet:Edge Guidance Network for Salient Object Detection},
 author={Zhao, Jia-Xing and Liu, Jiang-Jiang and Fan, Deng-Ping and Cao, Yang and Yang, Jufeng and Cheng, Ming-Ming},
 booktitle={The IEEE International Conference on Computer Vision (ICCV)},
 month={Oct},
 year={2019},
}

Contact

zhaojiaxing AT mail.nankai.edu.cn