Papers by Amirreza Shirani

3 papers
Let Me Choose: From Verbal Context to Font Selection (2020.acl-main)

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Challenge: Current font selection interfaces do not consider verbal context of the input text.
Approach: They propose a dataset containing examples of different topics in social media posts and ads, labeled through crowd-sourcing.
Outcome: The proposed model captures inter-subjectivity across annotations on a dataset of social media posts and ads labeled through crowd-sourcing.
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions (P19-1)

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Challenge: Visual communication relies heavily on images and short texts to grab a viewer's attention and convey a message in the most efficient way.
Approach: They propose a model that employs end-to-end label distribution learning on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity and ambiguity of the input.
Outcome: The proposed model captures the inter-subjectivity and ambiguity of the input and can be transformed to single-label learning by mapping labels to absolute labels via majority voting.

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