Zobrazeno 1 - 10
of 498
pro vyhledávání: '"Do, Tien"'
Autor:
Hesam-Shariati, Negin, Newton-John, Toby, Singh, Avinash K, Tirado Cortes, Carlos A, Do, Tien-Thong Nguyen, Craig, Ashley, Middleton, James W, Jensen, Mark P, Trost, Zina, Lin, Chin-Teng, Gustin, Sylvia M
Publikováno v:
JMIR Research Protocols, Vol 9, Iss 9, p e20979 (2020)
BackgroundNeuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing bod
Externí odkaz:
https://doaj.org/article/3f5992d726ea4deb99110aa5f378d59e
Autor:
Do, Tien, Sinha, Sudipta N.
Camera localization methods based on retrieval, local feature matching, and 3D structure-based pose estimation are accurate but require high storage, are slow, and are not privacy-preserving. A method based on scene landmark detection (SLD) was recen
Externí odkaz:
http://arxiv.org/abs/2401.18083
Previous studies showed that natural walking reduces the susceptibility to VR sickness. However, many users still experience VR sickness when wearing VR headsets that allow free walking in room-scale spaces. This paper studies VR sickness and postura
Externí odkaz:
http://arxiv.org/abs/2302.11129
Publikováno v:
Veterinary World, Vol 17, Iss 6, Pp 1196-1201 (2024)
Background and Aim: The African swine fever virus (ASFV), spanning 170–193 kb, contains over 200 proteins, including p72 and p30, which play crucial roles in the virus’s entry and expression. This study investigated the capability of detecting AS
Externí odkaz:
https://doaj.org/article/fdc6e27ad19643278c028e491be20683
Autor:
Vajda, Dániel László1 (AUTHOR) dvajda@hit.bme.hu, Do, Tien Van1 (AUTHOR), Bérczes, Tamás2 (AUTHOR), Farkas, Károly1 (AUTHOR)
Publikováno v:
Scientific Reports. 10/10/2024, Vol. 14 Issue 1, p1-22. 22p.
In this paper, we study a problem of egocentric scene understanding, i.e., predicting depths and surface normals from an egocentric image. Egocentric scene understanding poses unprecedented challenges: (1) due to large head movements, the images are
Externí odkaz:
http://arxiv.org/abs/2207.07077
Publikováno v:
In Atmospheric Environment 15 August 2024 331
Autor:
Grauman, Kristen, Westbury, Andrew, Byrne, Eugene, Chavis, Zachary, Furnari, Antonino, Girdhar, Rohit, Hamburger, Jackson, Jiang, Hao, Liu, Miao, Liu, Xingyu, Martin, Miguel, Nagarajan, Tushar, Radosavovic, Ilija, Ramakrishnan, Santhosh Kumar, Ryan, Fiona, Sharma, Jayant, Wray, Michael, Xu, Mengmeng, Xu, Eric Zhongcong, Zhao, Chen, Bansal, Siddhant, Batra, Dhruv, Cartillier, Vincent, Crane, Sean, Do, Tien, Doulaty, Morrie, Erapalli, Akshay, Feichtenhofer, Christoph, Fragomeni, Adriano, Fu, Qichen, Gebreselasie, Abrham, Gonzalez, Cristina, Hillis, James, Huang, Xuhua, Huang, Yifei, Jia, Wenqi, Khoo, Weslie, Kolar, Jachym, Kottur, Satwik, Kumar, Anurag, Landini, Federico, Li, Chao, Li, Yanghao, Li, Zhenqiang, Mangalam, Karttikeya, Modhugu, Raghava, Munro, Jonathan, Murrell, Tullie, Nishiyasu, Takumi, Price, Will, Puentes, Paola Ruiz, Ramazanova, Merey, Sari, Leda, Somasundaram, Kiran, Southerland, Audrey, Sugano, Yusuke, Tao, Ruijie, Vo, Minh, Wang, Yuchen, Wu, Xindi, Yagi, Takuma, Zhao, Ziwei, Zhu, Yunyi, Arbelaez, Pablo, Crandall, David, Damen, Dima, Farinella, Giovanni Maria, Fuegen, Christian, Ghanem, Bernard, Ithapu, Vamsi Krishna, Jawahar, C. V., Joo, Hanbyul, Kitani, Kris, Li, Haizhou, Newcombe, Richard, Oliva, Aude, Park, Hyun Soo, Rehg, James M., Sato, Yoichi, Shi, Jianbo, Shou, Mike Zheng, Torralba, Antonio, Torresani, Lorenzo, Yan, Mingfei, Malik, Jitendra
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers f
Externí odkaz:
http://arxiv.org/abs/2110.07058
Autor:
Vuong, Quang Tran, Jung, Keun-Sik, Kim, Seong-Joon, Kwon, Hye-Ok, Do, Tien Van, Lee, Ji Yi, Choi, Sung-Deuk
Publikováno v:
In Atmospheric Environment 1 June 2024 326
In this paper, we address the problem of estimating dense depth from a sequence of images using deep neural networks. Specifically, we employ a dense-optical-flow network to compute correspondences and then triangulate the point cloud to obtain an in
Externí odkaz:
http://arxiv.org/abs/2011.09594