Zobrazeno 1 - 10
of 4 126
pro vyhledávání: '"YE Sheng"'
Autor:
Kampman, Onno P., Phang, Ye Sheng, Han, Stanley, Xing, Michael, Hong, Xinyi, Hoosainsah, Hazirah, Tan, Caleb, Winata, Genta Indra, Wang, Skyler, Heaukulani, Creighton, Weng, Janice Huiqin, Morris, Robert JT
We introduce a general-purpose, human-in-the-loop dual dialogue system to support mental health care professionals. The system, co-designed with care providers, is conceptualized to assist them in interacting with care seekers rather than functioning
Externí odkaz:
http://arxiv.org/abs/2411.18429
Autor:
Ye, Sheng, He, Yuze, Lin, Matthieu, Sheng, Jenny, Fan, Ruoyu, Han, Yiheng, Hu, Yubin, Yi, Ran, Wen, Yu-Hui, Liu, Yong-Jin, Wang, Wenping
Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views. A few pioneering works have sought to tackle the challenge of sparse-view reconstruct
Externí odkaz:
http://arxiv.org/abs/2409.05474
3D Gaussian Splatting has recently emerged as a powerful representation that can synthesize remarkable novel views using consistent multi-view images as input. However, we notice that images captured in dark environments where the scenes are not full
Externí odkaz:
http://arxiv.org/abs/2408.09130
Publikováno v:
国際文化研究(オンライン版） = Journal of International Cultural Studies. 29:71-83
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=jairo_______::bbbc8c0e287a360c4b107a3b8c4378a7
http://hdl.handle.net/10097/00137050
http://hdl.handle.net/10097/00137050
Autor:
Sun, Zhiyao, Lv, Tian, Ye, Sheng, Lin, Matthieu, Sheng, Jenny, Wen, Yu-Hui, Yu, Minjing, Liu, Yong-Jin
The generation of stylistic 3D facial animations driven by speech presents a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion. However, existing methods either emp
Externí odkaz:
http://arxiv.org/abs/2310.00434
The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. Recent methods leverage neural radiance fields aided by predicted surfa
Externí odkaz:
http://arxiv.org/abs/2309.07640
We examine the motion of the massless scalar field and nearly bound null geodesics in the near-ring region of a black hole, which may possess either acceleration or a gravitomagnetic mass. Around such black holes, the photon ring deviates from the eq
Externí odkaz:
http://arxiv.org/abs/2309.02262
Autor:
Hu, Yubin, Ye, Sheng, Zhao, Wang, Lin, Matthieu, He, Yuze, Wen, Yu-Hui, He, Ying, Liu, Yong-Jin
Occlusion is a common issue in 3D reconstruction from RGB-D videos, often blocking the complete reconstruction of objects and presenting an ongoing problem. In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting mo
Externí odkaz:
http://arxiv.org/abs/2308.09591
Autor:
Nur Atiqah Othman, Chitra Panchapakesan, Siyuan Brandon Loh, Mila Zhang, Raj Kumar Gupta, Wijaya Martanto, Ye Sheng Phang, Robert J. T. Morris, Wai Chiong Loke, Kelvin Bryan Tan, Mythily Subramaniam, Yinping Yang
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Mental health issues have increased substantially since the onset of the COVID-19 pandemic. However, health policymakers do not have adequate data and tools to predict mental health demand, especially amid a crisis. Using time-series data co
Externí odkaz:
https://doaj.org/article/24f13c90bbb947c9ab64382d52fd6030
Autor:
Mingyu Wu, Kai Woon Goh, Kam Heng Chaw, Ye Sheng Koh, Marvin Dares, Che Fai Yeong, Eileen L. M. Su, Holderbaum William, Yunhui Zhang
Publikováno v:
Frontiers in Mechanical Engineering, Vol 10 (2024)
This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest model was evaluated against established models such as logistic regress
Externí odkaz:
https://doaj.org/article/428d2a112094419db965a99275844fae