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VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Conclusionby@kinetograph
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VEATIC: Video-based Emotion and Affect Tracking in Context Dataset: Conclusion

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In this paper, researchers introduce VEATIC dataset for human affect recognition, addressing limitations in existing datasets, enabling context-based inference.
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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]).

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.