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A Single Stream Network for Robust and Real-Time RGB-D Salient Object Detection

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal fusion between the RGB stream and the depth stream. They do not deeply explore the effect of the depth map itself. In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves a lightweight and real-time model. We tactfully utilize depth information from two perspectives: (1) Overcoming the incompatibility problem caused by the great difference between modalities, we build a single stream encoder to achieve the early fusion, which can take full advantage of ImageNet pre-trained backbone model to extract rich and discriminative features. (2) We design a novel depth-enhanced dual attention module (DEDA) to efficiently provide the fore-/back-ground branches with the spatially filtered features, which enables the decoder to optimally perform the middle fusion. Besides, we put forward a pyramidally attended feature extraction module (PAFE) to accurately localize the objects of different scales. Extensive experiments demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics. Furthermore, this model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a \(384 \times 384\) image.

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Acknowledgements

This work was supported in part by the National Key R&D Program of China #2018AAA0102003, National Natural Science Foundation of China #61876202, #61725202, #61751212 and #61829102, the Dalian Science and Technology Innovation Foundation #2019J12GX039, and the Fundamental Research Funds for the Central Universities # DUT20ZD212.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Xiaoqi Zhao, Lihe Zhang, Youwei Pang & Huchuan Lu

  2. Peng Cheng Laboratory, Shenzhen, China

    Huchuan Lu

  3. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, China

    Lei Zhang

  4. DAMO Academy, Alibaba Group, Hangzhou, China

    Lei Zhang

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  1. Xiaoqi Zhao
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  2. Lihe Zhang
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  3. Youwei Pang
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  4. Huchuan Lu
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  5. Lei Zhang
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Corresponding author

Correspondence to Lihe Zhang .

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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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Zhao, X., Zhang, L., Pang, Y., Lu, H., Zhang, L. (2020). A Single Stream Network for Robust and Real-Time 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 12367. Springer, Cham. https://doi.org/10.1007/978-3-030-58542-6_39

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  • DOI: https://doi.org/10.1007/978-3-030-58542-6_39

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