Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
In visual communication, text emphasis is used to increase the comprehension of written text to convey the author{'}s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author{'}s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.
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