VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Conclusion
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Authors:
(1) Zhihang Ren, University of California, Berkeley and these authors contributed equally to this work (Email: [email protected]);
(2) Jefferson Ortega, University of California, Berkeley and these authors contributed equally to this work (Email: [email protected]);
(3) Yifan Wang, University of California, Berkeley and these authors contributed equally to this work (Email: [email protected]);
(4) Zhimin Chen, University of California, Berkeley (Email: [email protected]);
(5) Yunhui Guo, University of Texas at Dallas (Email: [email protected]);
(6) Stella X. Yu, University of California, Berkeley and University of Michigan, Ann Arbor (Email: [email protected]);
(7) David Whitney, University of California, Berkeley (Email: [email protected]).
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Table of Links
Abstract and Intro
Related Wok
VEATIC Dataset
Experiments
Discussion
Conclusion
More About Stimuli
Annotation Details
Outlier Processing
Subject Agreement Across Videos
Familiarity and Enjoyment Ratings and References
6. Conclusion
In this study, we proposed the first context based large video dataset, VEATIC, for continuous valence and arousal prediction. Various visualizations show the diversity of our dataset and the consistency of our annotations. We also proposed a simple baseline algorithm to solve this challenge. Empirical results prove the effectiveness of our proposed method and the VEATIC dataset.
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This paper is available on arxiv under CC 4.0 license.
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