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Dynamically-Passed Contextual Information Network for Saliency Detection

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Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12307))

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Abstract

Nowadays, deep convolutional neural networks (CNNs) have made significant improvement in detecting salient objects by integrating multi-level convolutional features or exploiting the advantage of dilated convolution. However, how to construct finer structure to produce effective features for saliency detection is still a challenging task. In this paper, we propose a novel deep learning based network by dynamically incorporating multi-level feature maps. The proposed Dynamically-passed Contextual Information Network (DCI-Net) can effectively control the information passage process, incorporate multi-scale context information and alleviate the distraction of background noise to improve the performance of saliency detection. Specifically, we first integrate an effective Passage Unit (PU) that progressively incorporates the low-level information with the high-level cues, in order to preserve the boundary details of salient objects. Second, a Spatial Pyramid Dilation (SPD) module is used to enhance the multi-scale feature representation by using multiple convolution dilations to handle the varied size of salient objects. Finally, we apply a Residual Attention module (RA) to further reinforce the saliency detection. Quantitative and qualitative experiments demonstrate the effectiveness of the proposed framework. Our method can significantly improve the performance based on five popular benchmark datasets.

A. Dakhia—The author is a Ph.D. student in Dalian University of Technology.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Abdelhafid Dakhia, Tiantian Wang & Huchuan Lu

Authors
  1. Abdelhafid Dakhia
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  2. Tiantian Wang
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  3. Huchuan Lu
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Corresponding author

Correspondence to Huchuan Lu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

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Dakhia, A., Wang, T., Lu, H. (2020). Dynamically-Passed Contextual Information Network for Saliency Detection. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12307. Springer, Cham. https://doi.org/10.1007/978-3-030-60636-7_31

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  • DOI: https://doi.org/10.1007/978-3-030-60636-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60635-0

  • Online ISBN: 978-3-030-60636-7

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