@inproceedings{7d025825d0de45d4ac36d70a1770dcb4,
title = "Object-level salience detection by progressively enhanced network",
abstract = "Saliency detection plays an important role in computer vision area. However, most of the previous works focus on detecting the salient regions, rather than the objects, which is more reasonable in many practical applications. In this paper, a framework is proposed for detecting the salient objects in input images. This framework is composed of two main components: (1) progressively enhanced network (PEN) for amplifying the specified layers of the network and merging the global context simultaneously; (2) object-level boundary extraction module (OBEM) for extracting the complete boundary of the salient object. Experiments and comparisons show that the proposed framework achieves state-of-the-art results. Especially on many challenging datasets, our method performs much better than other methods.",
keywords = "Global context, Object-level boundary extract, Progressively enhanced network, Saliency detection",
author = "Wang Yuan and Haichuan Song and Xin Tan and Chengwei Chen and Shouhong Ding and Lizhuang Ma",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 28th International Conference on Artificial Neural Networks: Text and Time Series, ICANN 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
doi = "10.1007/978-3-030-30508-6\_30",
language = "英语",
isbn = "9783030305079",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "371--382",
editor = "Tetko, \{Igor V.\} and Pavel Karpov and Fabian Theis and Vera Kurkov{\'a}",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2019",
address = "德国",
}