Zobrazeno 1 - 10
of 2 267
pro vyhledávání: '"Cheung, Ka"'
Remarkable advancements in the recolorization of Neural Radiance Fields (NeRF) have simplified the process of modifying NeRF's color attributes. Yet, with the potential of NeRF to serve as shareable digital assets, there's a concern that malicious us
Externí odkaz:
http://arxiv.org/abs/2407.13390
Autor:
Zhou, Xingzhi, Dong, Xin, Li, Chunhao, Bai, Yuning, Xu, Yulong, Cheung, Ka Chun, See, Simon, Song, Xinpeng, Zhang, Runshun, Zhou, Xuezhong, Zhang, Nevin L.
Traditional Chinese medicine (TCM) relies on specific combinations of herbs in prescriptions to treat symptoms and signs, a practice that spans thousands of years. Predicting TCM prescriptions presents a fascinating technical challenge with practical
Externí odkaz:
http://arxiv.org/abs/2407.10510
Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation. With the rising prominence and influence of NeRF, safeguarding its intellectual property has become increasingly important. In this paper, we propose \textbf{NeRFPro
Externí odkaz:
http://arxiv.org/abs/2407.07735
Autor:
Du, Wenyu, Cheng, Shuang, Luo, Tongxu, Qiu, Zihan, Huang, Zeyu, Cheung, Ka Chun, Cheng, Reynold, Fu, Jie
Language models (LMs) exhibit impressive performance and generalization capabilities. However, LMs struggle with the persistent challenge of catastrophic forgetting, which undermines their long-term sustainability in continual learning (CL). Existing
Externí odkaz:
http://arxiv.org/abs/2406.17245
Autor:
Cheung, Ka Lung, Lee, Chi Chung
The adoption of Building Information Modeling (BIM) is beneficial in construction projects. However, it faces challenges due to the lack of a unified and scalable framework for converting 3D model details into BIM. This paper introduces SRBIM, a unif
Externí odkaz:
http://arxiv.org/abs/2406.01480
Autor:
Cheung, Ka Lung, Lee, Chi Chung
Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment. Nevertheless, existing outdoor 3D point cloud datasets have limi
Externí odkaz:
http://arxiv.org/abs/2406.01337
Autor:
Guo, Qiushan, De Mello, Shalini, Yin, Hongxu, Byeon, Wonmin, Cheung, Ka Chun, Yu, Yizhou, Luo, Ping, Liu, Sifei
Vision language models (VLMs) have experienced rapid advancements through the integration of large language models (LLMs) with image-text pairs, yet they struggle with detailed regional visual understanding due to limited spatial awareness of the vis
Externí odkaz:
http://arxiv.org/abs/2403.02330
Autor:
Nekliudov, Nikita A, Blyuss, Oleg, Cheung, Ka Yan, Petrou, Loukia, Genuneit, Jon, Sushentsev, Nikita, Levadnaya, Anna, Comberiati, Pasquale, Warner, John O, Tudor-Williams, Gareth, Teufel, Martin, Greenhawt, Matthew, DunnGalvin, Audrey, Munblit, Daniel
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 9, p e20955 (2020)
BackgroundThe COVID-19 pandemic has potentially had a negative impact on the mental health and well-being of individuals and families. Anxiety levels and risk factors within particular populations are poorly described. ObjectiveThis study aims to ev
Externí odkaz:
https://doaj.org/article/66f3543df088489b94de3c623d5987fa
Autor:
Shi, Xiaoyu, Huang, Zhaoyang, Wang, Fu-Yun, Bian, Weikang, Li, Dasong, Zhang, Yi, Zhang, Manyuan, Cheung, Ka Chun, See, Simon, Qin, Hongwei, Dai, Jifeng, Li, Hongsheng
We introduce Motion-I2V, a novel framework for consistent and controllable image-to-video generation (I2V). In contrast to previous methods that directly learn the complicated image-to-video mapping, Motion-I2V factorizes I2V into two stages with exp
Externí odkaz:
http://arxiv.org/abs/2401.15977
Test-time domain adaptation effectively adjusts the source domain model to accommodate unseen domain shifts in a target domain during inference. However, the model performance can be significantly impaired by continuous distribution changes in the ta
Externí odkaz:
http://arxiv.org/abs/2401.14619