Abstract
Since the fully convolutional networks was proposed, it has made great progress in the salient object detection. However, this kind of network structure still has obvious problems of incomplete salient objects segmentation and redundant information. Therefore, this paper propose a novel network model named Multi-scale Dense Network (MSDNet) to solve the above problems. First, we designed a multi-receptive enhancement and supplementation module(MRES), which increases the discriminability of features through feature interaction under different receptive fields. Second, we design a network framework MSDNet that first uses dense feature interactions and a pyramid-shaped feature fusion structure to get enhanced features and better features fusion. Experimental results on five benchmark datasets demonstrate the proposed method against 11 state-of-the-art approaches, it can effectively improve the completeness of salient objects and suppress background information.
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Zhang, H., Zhao, X., Yang, C., Li, Y., Wang, R. (2022). MSDNet: Multi-scale Dense Networks for Salient Object Detection. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_26
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DOI: https://doi.org/10.1007/978-3-031-18916-6_26
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