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VEATIC: Familiarity and Enjoyment Ratings & References

by Kinetograph: The Video Editing Technology PublicationMay 27th, 2024

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

featured image - VEATIC: Familiarity and Enjoyment Ratings & References

This paper is available on arxiv under CC 4.0 license.

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

11. Familiarity and Enjoyment Ratings

Familiarity and enjoyment ratings were collected for each video across participants, as shown in Figure 13. Familiarity and enjoyment ratings for video IDs 0-83 were collected in a scale of 1-5 and 1-9, respectively. Familiarity and enjoyment ratings for video IDs 83-123 were collected prior to the planning of the VEATIC dataset and were collected on a different scale. Familiarity and enjoyment ratings for video IDs 83-97 were collected on a scale of 0- 5 and familiarity/enjoyment ratings were not collected for video IDs 98-123. For analysis and visualization purposes, we rescaled the familiarity and enjoyment ratings for video IDs 83-97 to 1-5 and 1-9, respectively, to match video IDs 0-83. To rescale the familiarity values from 0-5 to 1-5 we performed a linear transformation, we first normalized the data between 0 and 1, then we multiplied the values by 4 and added 1. We rescaled the enjoyment values from 0-5 to 1-9 similarly by first normalizing the data between 0 and 1, then we multiplied the values by 8 and added 1. As a result, the average familiarity rating was 1.61 while the average enjoyment rating was 4.98 for video IDs 0-97.

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