Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Feng, Andrew"'
Autor:
Gao, Zhiyuan, Teng, Wenbin, Chen, Gonglin, Wu, Jinsen, Xu, Ningli, Qin, Rongjun, Feng, Andrew, Zhao, Yajie
Integrating aerial imagery-based scene generation into applications like autonomous driving and gaming enhances realism in 3D environments, but challenges remain in creating detailed content for occluded areas and ensuring real-time, consistent rende
Externí odkaz:
http://arxiv.org/abs/2409.16685
Autor:
Chen, Gonglin, Wu, Jinsen, Chen, Haiwei, Teng, Wenbin, Gao, Zhiyuan, Feng, Andrew, Qin, Rongjun, Zhao, Yajie
Establishing consistent and dense correspondences across multiple images is crucial for Structure from Motion (SfM) systems. Significant view changes, such as air-to-ground with very sparse view overlap, pose an even greater challenge to the correspo
Externí odkaz:
http://arxiv.org/abs/2409.02310
Autor:
Roth, Holger R., Beutel, Daniel J., Cheng, Yan, Marques, Javier Fernandez, Pan, Heng, Chen, Chester, Zhang, Zhihong, Wen, Yuhong, Yang, Sean, Isaac, Yang, Hsieh, Yuan-Ting, Xu, Ziyue, Xu, Daguang, Lane, Nicholas D., Feng, Andrew
Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation
Externí odkaz:
http://arxiv.org/abs/2407.00031
3D Gaussian Splatting (3DGS) has recently advanced radiance field reconstruction by offering superior capabilities for novel view synthesis and real-time rendering speed. However, its strategy of blending optimization and adaptive density control mig
Externí odkaz:
http://arxiv.org/abs/2405.12369
Autor:
Roth, Holger R., Xu, Ziyue, Hsieh, Yuan-Ting, Renduchintala, Adithya, Yang, Isaac, Zhang, Zhihong, Wen, Yuhong, Yang, Sean, Lu, Kevin, Kersten, Kristopher, Ricketts, Camir, Xu, Daguang, Chen, Chester, Cheng, Yan, Feng, Andrew
In the ever-evolving landscape of artificial intelligence (AI) and large language models (LLMs), handling and leveraging data effectively has become a critical challenge. Most state-of-the-art machine learning algorithms are data-centric. However, as
Externí odkaz:
http://arxiv.org/abs/2402.07792
In this paper, we propose an Instant Photorealistic Style Transfer (IPST) approach, designed to achieve instant photorealistic style transfer on super-resolution inputs without the need for pre-training on pair-wise datasets or imposing extra constra
Externí odkaz:
http://arxiv.org/abs/2309.10011
Autor:
Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perfor
Externí odkaz:
http://arxiv.org/abs/2307.15208
Autor:
Diaz-Pinto, Andres, Mehta, Pritesh, Alle, Sachidanand, Asad, Muhammad, Brown, Richard, Nath, Vishwesh, Ihsani, Alvin, Antonelli, Michela, Palkovics, Daniel, Pinter, Csaba, Alkalay, Ron, Pieper, Steve, Roth, Holger R., Xu, Daguang, Dogra, Prerna, Vercauteren, Tom, Feng, Andrew, Quraini, Abood, Ourselin, Sebastien, Cardoso, M. Jorge
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2305.10655
Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robot
Externí odkaz:
http://arxiv.org/abs/2303.12822
Autor:
Cardoso, M. Jorge, Li, Wenqi, Brown, Richard, Ma, Nic, Kerfoot, Eric, Wang, Yiheng, Murrey, Benjamin, Myronenko, Andriy, Zhao, Can, Yang, Dong, Nath, Vishwesh, He, Yufan, Xu, Ziyue, Hatamizadeh, Ali, Zhu, Wentao, Liu, Yun, Zheng, Mingxin, Tang, Yucheng, Yang, Isaac, Zephyr, Michael, Hashemian, Behrooz, Alle, Sachidanand, Darestani, Mohammad Zalbagi, Budd, Charlie, Modat, Marc, Vercauteren, Tom, Wang, Guotai, Li, Yiwen, Hu, Yipeng, Fu, Yunguan, Gorman, Benjamin, Johnson, Hans, Genereaux, Brad, Erdal, Barbaros S., Gupta, Vikash, Diaz-Pinto, Andres, Dourson, Andre, Maier-Hein, Lena, Jaeger, Paul F., Baumgartner, Michael, Kalpathy-Cramer, Jayashree, Flores, Mona, Kirby, Justin, Cooper, Lee A. D., Roth, Holger R., Xu, Daguang, Bericat, David, Floca, Ralf, Zhou, S. Kevin, Shuaib, Haris, Farahani, Keyvan, Maier-Hein, Klaus H., Aylward, Stephen, Dogra, Prerna, Ourselin, Sebastien, Feng, Andrew
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be use
Externí odkaz:
http://arxiv.org/abs/2211.02701