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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 445–462Cite as

Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12372))

Abstract

In this paper, we introduce a novel network, called discriminative feature network (DFNet), to address the unsupervised video object segmentation task. To capture the inherent correlation among video frames, we learn discriminative features (D-features) from the input images that reveal feature distribution from a global perspective. The D-features are then used to establish correspondence with all features of test image under conditional random field (CRF) formulation, which is leveraged to enforce consistency between pixels. The experiments verify that DFNet outperforms state-of-the-art methods by a large margin with a mean IoU score of 83.4% and ranks first on the DAVIS-2016 leaderboard while using much fewer parameters and achieving much more efficient performance in the inference phase. We further evaluate DFNet on the FBMS dataset and the video saliency dataset ViSal, reaching a new state-of-the-art. To further demonstrate the generalizability of our framework, DFNet is also applied to the image object co-segmentation task. We perform experiments on a challenging dataset PASCAL-VOC and observe the superiority of DFNet. The thorough experiments verify that DFNet is able to capture and mine the underlying relations of images and discover the common foreground objects.

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Acknowledgments

This work is supported by Hong Kong RGC GRF 16206819, Hong Kong RGC GRF 16203518 and Hong Kong T22-603/15N.

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Authors and Affiliations

  1. Hong Kong University of Science and Technology, Kowloon City, Hong Kong

    Mingmin Zhen, Lei Zhou, Jiaxiang Shang, Haoan Feng & Long Quan

  2. Everest Innovation Technology, Kowloon City, Hong Kong

    Shiwei Li & Tian Fang

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  1. Mingmin Zhen
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  2. Shiwei Li
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  3. Lei Zhou
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  4. Jiaxiang Shang
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Correspondence to Mingmin Zhen .

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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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Zhen, M. et al. (2020). Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_27

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  • DOI: https://doi.org/10.1007/978-3-030-58583-9_27

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