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    Social image parsing by cross-modal data refinement

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    Authors
    Lu, Zhiwu
    Gao, Xin cc
    Huang, Songfang
    Wang, Liwei
    Wen, Ji-Rong
    Date
    2015-06
    Permanent link to this record
    http://hdl.handle.net/10754/630857
    
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    Abstract
    This paper presents a cross-modal data refinement algorithm for social image parsing, or segmenting all the objects within a social image and then identifying their categories. Different from the traditional fully supervised image parsing that takes pixel-level labels as strong supervisory information, our social image parsing is initially provided with the noisy tags of images (i.e. image-level labels), which are shared by social users. By over-segmenting each image into multiple regions, we formulate social image parsing as a cross-modal data refinement problem over a large set of regions, where the initial labels of each region are inferred from image-level labels. Furthermore, we develop an efficient algorithm to solve such cross-modal data refinement problem. The experimental results on several benchmark datasets show the effectiveness of our algorithm. More notably, our algorithm can be considered to provide an alternative and natural way to address the challenging problem of image parsing, since image-level labels are much easier to access than pixel-level labels.
    Citation
    Zhiwu Lu, Xin Gao, Songfang Huang, Liwei Wang, and Ji-Rong Wen. 2015. Social image parsing by cross-modal data refinement. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI'15), Qiang Yang and Michael Wooldridge (Eds.). AAAI Press 2169-2175.
    Journal
    Proceedings of the 24th International Conference on Artificial Intelligence
    Conference/Event name
    Twenty-Fourth International Joint Conference on Artificial Intelligence
    Additional Links
    https://dl.acm.org/citation.cfm?id=2832550
    https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/viewPaper/10765
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