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    • 39-Issue 7
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    Coarse to Fine:Weak Feature Boosting Network for Salient Object Detection

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    Date
    2020
    Author
    Zhang, Chenhao ORCID
    Gao, Shanshan
    Pan, Xiao
    Wang, Yuting
    Zhou, Yuanfeng
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    Abstract
    Salient object detection is to identify objects or regions with maximum visual recognition in an image, which brings significant help and improvement to many computer visual processing tasks. Although lots of methods have occurred for salient object detection, the problem is still not perfectly solved especially when the background scene is complex or the salient object is small. In this paper, we propose a novel Weak Feature Boosting Network (WFBNet) for the salient object detection task. In the WFBNet, we extract the unpredictable regions (low confidence regions) of the image via a polynomial function and enhance the features of these regions through a well-designed weak feature boosting module (WFBM). Starting from a coarse saliency map, we gradually refine it according to the boosted features to obtain the final saliency map, and our network does not need any post-processing step. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our algorithm has considerable advantages over the existing state-of-the-art methods.
    BibTeX
    @article {10.1111:cgf.14155,
    journal = {Computer Graphics Forum},
    title = {{Coarse to Fine:Weak Feature Boosting Network for Salient Object Detection}},
    author = {Zhang, Chenhao and Gao, Shanshan and Pan, Xiao and Wang, Yuting and Zhou, Yuanfeng},
    year = {2020},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.14155}
    }
    URI
    https://doi.org/10.1111/cgf.14155
    https://diglib.eg.org:443/handle/10.1111/cgf14155
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA