GIFGIF+: Collecting emotional animated GIFs with clustered multi-task learning

Animated GIFs are widely used on the Internet to express emotions, but their automatic analysis is largely unexplored. Existing GIF datasets with emotion labels are too small for training contemporary machine learning models, so we propose a semi-automatic method to collect emotional animated GIFs f...

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Bibliographic Details
Published inInternational Conference on Affective Computing and Intelligent Interaction and workshops pp. 510 - 517
Main Authors Chen, Weixuan, Rudovic, Ognjen Oggi, Picard, Rosalind W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Affective computing
Emotion recognition
Internet
Pipelines
Videos
Visualization
Online AccessGet full text
ISSN2156-8111
DOI10.1109/ACII.2017.8273647

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Summary:Animated GIFs are widely used on the Internet to express emotions, but their automatic analysis is largely unexplored. Existing GIF datasets with emotion labels are too small for training contemporary machine learning models, so we propose a semi-automatic method to collect emotional animated GIFs from the Internet with the least amount of human labor. The method trains weak emotion recognizers on labeled data, and uses them to sort a large quantity of unlabeled GIFs. We found that by exploiting the clustered structure of emotions, the number of GIFs a labeler needs to check can be greatly reduced. Using the proposed method, a dataset called GIFGIF+ with 23,544 GIFs over 17 emotions was created, which provides a promising platform for affective computing research.
ISSN:2156-8111
DOI:10.1109/ACII.2017.8273647