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dc.contributor.authorZhang, Chenhaoen_US
dc.contributor.authorGao, Shanshanen_US
dc.contributor.authorPan, Xiaoen_US
dc.contributor.authorWang, Yutingen_US
dc.contributor.authorZhou, Yuanfengen_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.date.accessioned2020-10-29T18:51:00Z
dc.date.available2020-10-29T18:51:00Z
dc.date.issued2020
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14155
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14155
dc.description.abstractSalient 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectInterest point and salient region detections
dc.titleCoarse to Fine:Weak Feature Boosting Network for Salient Object Detectionen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersTracking and Saliency
dc.description.volume39
dc.description.number7
dc.identifier.doi10.1111/cgf.14155
dc.identifier.pages411-420


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  • 39-Issue 7
    Pacific Graphics 2020 - Symposium Proceedings

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