Styling Words: A Simple and Natural Way to Increase Variability in Training Data Collection for Gesture Recognition

要旨

Due to advances in deep learning, gestures have become a more common tool for human-computer interaction. When implementing a large amount of training data, deep learning models show remarkable performance in gesture recognition. Since it is expensive and time consuming to collect gesture data from people, we are often confronted with a practicality issue when managing the quantity and quality of training data. It is a well-known fact that increasing training data variability can help to improve the generalization performance of machine learning models. Thus, we directly intervene in the collection of gesture data to increase human gesture variability by adding some words (called styling words) into the data collection instructions, e.g., giving the instruction "perform gesture #1 faster" as opposed to "perform gesture #1." Through an in-depth analysis of gesture features and video-based gesture recognition, we have confirmed the advantageous use of styling words in gesture training data collection.

著者
Woojin Kang
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
In-Taek Jung
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
DaeHo Lee
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
Jin-Hyuk Hong
Gwangju Institute of Science and Technology, Gwangju, Korea, Republic of
DOI

10.1145/3411764.3445457

論文URL

https://doi.org/10.1145/3411764.3445457

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Engineering Development Support

[A] Paper Room 05, 2021-05-10 17:00:00~2021-05-10 19:00:00 / [B] Paper Room 05, 2021-05-11 01:00:00~2021-05-11 03:00:00 / [C] Paper Room 05, 2021-05-11 09:00:00~2021-05-11 11:00:00
Paper Room 05
14 件の発表
2021-05-10 17:00:00
2021-05-10 19:00:00
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