Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Li, Runjia"'
Autor:
Yang, Ling, Zhang, Zixiang, Han, Junlin, Zeng, Bohan, Li, Runjia, Torr, Philip, Zhang, Wentao
Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. Due to the scarcity of 3D data, state-of-the-art approaches utilize pre-trained 2D diffusion priors, optimized through S
Externí odkaz:
http://arxiv.org/abs/2410.09009
Autor:
Li, Runjia, Han, Junlin, Melas-Kyriazi, Luke, Sun, Chunyi, An, Zhaochong, Gui, Zhongrui, Sun, Shuyang, Torr, Philip, Jakab, Tomas
We present DreamBeast, a novel method based on score distillation sampling (SDS) for generating fantastical 3D animal assets composed of distinct parts. Existing SDS methods often struggle with this generation task due to a limited understanding of p
Externí odkaz:
http://arxiv.org/abs/2409.08271
In recent years, precision treatment strategy have gained significant attention in medical research, particularly for patient care. We propose a novel framework for estimating conditional average treatment effects (CATE) in time-to-event data with co
Externí odkaz:
http://arxiv.org/abs/2407.18389
Autor:
Gui, Zhongrui, Sun, Shuyang, Li, Runjia, Yuan, Jianhao, An, Zhaochong, Roth, Karsten, Prabhu, Ameya, Torr, Philip
Continual segmentation has not yet tackled the challenge of improving open-vocabulary segmentation models with training data for accurate segmentation across large, continually expanding vocabularies. We discover that traditional continual training r
Externí odkaz:
http://arxiv.org/abs/2404.09447
Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive, which limits the number of categories in segmentation datasets. Consequently, the vocabulary ca
Externí odkaz:
http://arxiv.org/abs/2312.07661
This paper presents OxfordTVG-HIC (Humorous Image Captions), a large-scale dataset for humour generation and understanding. Humour is an abstract, subjective, and context-dependent cognitive construct involving several cognitive factors, making it a
Externí odkaz:
http://arxiv.org/abs/2307.11636
2-in-1 design (Chen et al. 2018) is becoming popular in oncology drug development, with the flexibility of using different endpoints at different decision time. Based on the observed interim data, sponsors choose either to seamlessly advance a small
Externí odkaz:
http://arxiv.org/abs/2212.11433
In this paper, we address the problem of blind deblurring with high efficiency. We propose a set of lightweight deep-wiener-network to finish the task with real-time speed. The Network contains a deep neural network for estimating parameters of wiene
Externí odkaz:
http://arxiv.org/abs/2211.16356
Autor:
Imani, Ehsan, Zhang, Guojun, Li, Runjia, Luo, Jun, Poupart, Pascal, Torr, Philip H. S., Pan, Yangchen
Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix. Drawing inspiration from this observation,
Externí odkaz:
http://arxiv.org/abs/2211.14960
Autor:
Lichtsinn, Katrin C., Church, Joseph T., Waltz, Paul K., Azzuqa, Abeer, Graham, Jacqueline, Troutman, Jennifer, Li, Runjia, Mahmood, Burhan
Publikováno v:
In Journal of Pediatric Surgery March 2024 59(3):451-458