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Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12353))

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Abstract

In this paper, we aim to develop an efficient and compact deep network for RGB-D salient object detection, where the depth image provides complementary information to boost performance in complex scenarios. Starting from a coarse initial prediction by a multi-scale residual block, we propose a progressively guided alternate refinement network to refine it. Instead of using ImageNet pre-trained backbone network, we first construct a lightweight depth stream by learning from scratch, which can extract complementary features more efficiently with less redundancy. Then, different from the existing fusion based methods, RGB and depth features are fed into proposed guided residual (GR) blocks alternately to reduce their mutual degradation. By assigning progressive guidance in the stacked GR blocks within each side-output, the false detection and missing parts can be well remedied. Extensive experiments on seven benchmark datasets demonstrate that our model outperforms existing state-of-the-art approaches by a large margin, and also shows superiority in efficiency (71 FPS) and model size (64.9 MB).

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Acknowledgments

This research was supported by the National Nature Science Foundation of China (No. 61802336) and China Scholarship Council (CSC) Program. This work was mainly done when Shuhan Chen was visiting Northeastern University as a visiting scholar.

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Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, China

    Shuhan Chen

  2. Department of ECE and Khoury College of Computer Science, Northeastern University, Boston, USA

    Yun Fu

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  1. Shuhan Chen
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  2. Yun Fu
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Correspondence to Shuhan Chen .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Chen, S., Fu, Y. (2020). Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12353. Springer, Cham. https://doi.org/10.1007/978-3-030-58598-3_31

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  • DOI: https://doi.org/10.1007/978-3-030-58598-3_31

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