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• W2251390968 abstract "A key task to understand an image and itscorresponding caption is not only to findout what is shown on the picture and describedin the text, but also what is theexact relationship between these two elements.The long-term objective of ourwork is to be able to distinguish differenttypes of relationship, including literalvs. non-literal usages, as well as finegrainednon-literal usages (i.e., symbolicvs. iconic). Here, we approach this challengingproblem by answering the question:‘How can we quantify the degreesof similarity between the literal meaningsexpressed within images and their captions?’.We formulate this problem as aranking task, where links between entitiesand potential regions are created andranked for relevance. Using a RankingSVM allows us to leverage from the preferenceordering of the links, which help usin the similarity calculation for the casesof visual or textual ambiguity, as well asmisclassified data. Our experiments showthat aggregating different features using asupervised ranker achieves better resultsthan a baseline knowledge-base method.However, much work still lies ahead, andwe accordingly conclude the paper with adetailed discussion of a short- and longtermoutlook on how to push our work onrelationship classification one step further." @default.
• W2251390968 created "2016-06-24" @default.
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• W2251390968 date "2015-01-01" @default.
• W2251390968 modified "2023-09-23" @default.
• W2251390968 title "Image with a Message: Towards Detecting Non-Literal Image Usages by Visual Linking" @default.
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• W2251390968 doi "https://doi.org/10.18653/v1/w15-2808" @default.
• W2251390968 hasPublicationYear "2015" @default.
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