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VEATIC: Familiarity and Enjoyment Ratings & Referencesby@kinetograph
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VEATIC: Familiarity and Enjoyment Ratings & References

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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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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]).

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.


Figure 11. Example of different ratings of the same video in VEATIC. (a). The two selected characters. (b). The continuous emotion ratings of corresponding characters. The same color indicates the same character. A good emotion recognition algorithm should infer the emotion of two characters correspondingly given the interactions between characters and the exact same context information.

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Figure 12. a) Five annotators’ response standard deviation vs. all annotators’ response standard deviation. Testing a small number of annotators can lead to substantial imprecision in annotations. Increasing the number of annotators, as in this study, greatly improves precision. b) Annotators’ response standard deviation for each video. Red and blue solid lines indicate the standard deviation of annotators’ responses for valence and arousal, in each video, respectively. The results are sorted based on the standard deviation of each video for visualization purposes. The dashed lines show the median standard deviation for each dimension. The mean values for standard deviations of valence and arousal are the same with µ = 0.248.


Figure 13. Familiarity and enjoyment ratings across all videos. Each bar represents the average familiarity or enjoyment rating reported by all participants who annotated the video. The average rating across all videos is depicted by the horizontal dashed line in both figures. Video IDs are shown on the x-axis.


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This paper is available on arxiv under CC 4.0 license.