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]).
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.
This paper is available on arxiv under CC 4.0 license.