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pro vyhledávání: '"Lee, Jaeyeon"'
To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily pred
Externí odkaz:
http://arxiv.org/abs/2211.00930
Autor:
Jin Jun, Su, Lee, Jinhong, Ryu, Myung-Hyun, Lee, Moonwon, Lee, Jaeyeon, Kim, Hansung, Yim, Kanghoon, Jung, Kyu-Nam
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497
Autor:
Zhang, Gang-Feng, Azorin-Molina, Cesar, Chen, Deliang, McVicar, Tim R., Guijarro, Jose A., Deng, Kai-Qiang, Minola, Lorenzo, Lee, Jaeyeon, Son, Seok-Woo, Ma, Heng, Shi, Pei-Jun
Publikováno v:
In Advances in Climate Change Research June 2024 15(3):525-536
This paper introduces a large-scale Korean speech dataset, called VOTE400, that can be used for analyzing and recognizing voices of the elderly people. The dataset includes about 300 hours of continuous dialog speech and 100 hours of read speech, bot
Externí odkaz:
http://arxiv.org/abs/2101.11469
Autor:
Yoon, Youngwoo, Cha, Bok, Lee, Joo-Haeng, Jang, Minsu, Lee, Jaeyeon, Kim, Jaehong, Lee, Geehyuk
For human-like agents, including virtual avatars and social robots, making proper gestures while speaking is crucial in human--agent interaction. Co-speech gestures enhance interaction experiences and make the agents look alive. However, it is diffic
Externí odkaz:
http://arxiv.org/abs/2009.02119
Publikováno v:
INT J ROBOT RES 40.4-5 (2021) 691-697
To better interact with users, a social robot should understand the users' behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and im
Externí odkaz:
http://arxiv.org/abs/2009.02041
ETRI-Activity3D: A Large-Scale RGB-D Dataset for Robots to Recognize Daily Activities of the Elderly
Deep learning, based on which many modern algorithms operate, is well known to be data-hungry. In particular, the datasets appropriate for the intended application are difficult to obtain. To cope with this situation, we introduce a new dataset calle
Externí odkaz:
http://arxiv.org/abs/2003.01920
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Training an object instance detector where only a few training object images are available is a challenging task. One solution is a cut-and-paste method that generates a training dataset by cutting object areas out of training images and pasting them
Externí odkaz:
http://arxiv.org/abs/1909.11972
Akademický článek
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