Abstract
Currently, fully convolutional network based salient object detection approaches have some challenging problems. This paper proposes a novel salient object detection approach using global context and multi-scale feature representation to estimate saliency maps in a pixel-wise manner. Firstly, we explore and design a multi-scale feature enhancement module to improve the capability of feature representation and learning of multi-level side-output features. Moreover, we use global features to guide side-output multi-scale features to focus on the useful information, which could help the network effectively locate salient objects and suppress background noises. Finally, the feature pyramid network structure is utilized to refine the estimated results in a coarse-to-fine manner, and then obtain the final predicted results. The comparisons of our approach and 15 state-of-the-art methods demonstrate the effictiveness and robustness of the proposed approach on various scenarios.
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This work is supported by Natural Science Foundation of Shanghai under Grant Nos. 19ZR1455300 and 21ZR1462600.
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Zhao, Z., Fang, Y., Zhang, Q., Chen, X., Dai, M., Lin, J. (2022). Global Context Guided Multi-scale Feature Network for Salient Object Detection. In: Deng, Z. (eds) Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-16-6372-7_10
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DOI: https://doi.org/10.1007/978-981-16-6372-7_10
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