@inproceedings{bbb08f02cdf24c3fb26a1a672f30ab24,
title = "Saliency Detection by Deep Network with Boundary Refinement and Global Context",
abstract = "A novel end-to-end fully convolutional neural network for saliency detection is proposed in this paper, aiming at refining the boundary and covering the global context (GBR-Net). Previous CNN based methods for saliency detection are universally accompanied with blurring edge and ambiguous salient object. To tackle this problem, we propose to embed the boundary enhancement block (BEB) into the network to refine edge. It keeps the details by the mutual-coupling con-volutionallayers. Besides, we employ a pooling pyramid that utilizes the multi-level feature informations to search global context, and it also contributes as an auxiliary supervision. The final saliency map is obtained by fusing the edge refinement with global context extraction. Experiments on four benchmark datasets prove that the proposed saliency detection model gains an edge over the state-of-the-art approaches.",
keywords = "Boundary refinement, Global context, Pooling pyramid, Saliency detection",
author = "Xin Tan and Hengliang Zhu and Zhiwen Shao and Xiaonan Hou and Yangyang Hao and Lizhuang Ma",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Multimedia and Expo, ICME 2018 ; Conference date: 23-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "8",
doi = "10.1109/ICME.2018.8486572",
language = "英语",
series = "Proceedings - IEEE International Conference on Multimedia and Expo",
publisher = "IEEE Computer Society",
booktitle = "2018 IEEE International Conference on Multimedia and Expo, ICME 2018",
address = "美国",
}