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.
This work was supported by Pudong NewArea Science and Technology Development Fund (PKJ2018-Y46), the Science and Technology Commission of Shanghai Municipality Program (No. 18D1205903), the National Social Science Foundation of China (No. 18ZD22), and Multidisciplinary Project of Shanghai Jiao Tong University. It was also partially supported by the joint project of Tencent YouTu and East China Normal University.
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Yuan, W., Song, H., Tan, X., Chen, C., Ding, S., Ma, L. (2019). Object-Level Salience Detection by Progressively Enhanced Network. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_30
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