Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Shen, Junxiao"'
Training a real-time gesture recognition model heavily relies on annotated data. However, manual data annotation is costly and demands substantial human effort. In order to address this challenge, we propose a framework that can automatically annotat
Externí odkaz:
http://arxiv.org/abs/2401.11150
Autor:
Shen, Junxiao, De Lange, Matthias, Xu, Xuhai "Orson", Zhou, Enmin, Tan, Ran, Suda, Naveen, Lazarewicz, Maciej, Kristensson, Per Ola, Karlson, Amy, Strasnick, Evan
Providing users with accurate gestural interfaces, such as gesture recognition based on wrist-worn devices, is a key challenge in mixed reality. However, static machine learning processes in gesture recognition assume that training and test data come
Externí odkaz:
http://arxiv.org/abs/2401.11144
Text entry is an essential task in our day-to-day digital interactions. Numerous intelligent features have been developed to streamline this process, making text entry more effective, efficient, and fluid. These improvements include sentence predicti
Externí odkaz:
http://arxiv.org/abs/2310.08101
We depend on our own memory to encode, store, and retrieve our experiences. However, memory lapses can occur. One promising avenue for achieving memory augmentation is through the use of augmented reality head-mounted displays to capture and preserve
Externí odkaz:
http://arxiv.org/abs/2308.05822
Autor:
Xu, Xuhai, Yu, Mengjie, Jonker, Tanya R., Todi, Kashyap, Lu, Feiyu, Qian, Xun, Belo, João Marcelo Evangelista, Wang, Tianyi, Li, Michelle, Mun, Aran, Wu, Te-Yen, Shen, Junxiao, Zhang, Ting, Kokhlikyan, Narine, Wang, Fulton, Sorenson, Paul, Kim, Sophie Kahyun, Benko, Hrvoje
Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequentl
Externí odkaz:
http://arxiv.org/abs/2303.16292
We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian c
Externí odkaz:
http://arxiv.org/abs/2202.06557
Autor:
Shen, Junxiao, Hao, Yi, Yan, Yangxi, Li, Zhimin, Wang, Pangpang, Murakami, Ri-ichi, Zhang, Dongyan
Publikováno v:
In Journal of Electroanalytical Chemistry 1 September 2024 968
Deep learning approaches deliver state-of-the-art performance in recognition of spatiotemporal human motion data. However, one of the main challenges in these recognition tasks is limited available training data. Insufficient training data results in
Externí odkaz:
http://arxiv.org/abs/2105.13061
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Akademický článek
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