Zobrazeno 1 - 10
of 5 135
pro vyhledávání: '"Qi, Yue A"'
This paper introduces a novel continual learning framework for synthesising novel views of multiple scenes, learning multiple 3D scenes incrementally, and updating the network parameters only with the training data of the upcoming new scene. We build
Externí odkaz:
http://arxiv.org/abs/2409.04482
3D Gaussian splatting (3DGS) has recently demonstrated promising advancements in RGB-D online dense mapping. Nevertheless, existing methods excessively rely on per-pixel depth cues to perform map densification, which leads to significant redundancy a
Externí odkaz:
http://arxiv.org/abs/2408.17223
Novel View Synthesis (NVS) from unconstrained photo collections is challenging in computer graphics. Recently, 3D Gaussian Splatting (3DGS) has shown promise for photorealistic and real-time NVS of static scenes. Building on 3DGS, we propose an effic
Externí odkaz:
http://arxiv.org/abs/2406.02407
For binary source transmission, this paper proposes an element-pair (EP) coding scheme for supporting sourced massive random access, which is used to solve the finite blocklength (FBL) of multiuser reliability transmission problem. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2405.11734
Autor:
Liu, Yong, Kang, Mengtian, Gao, Shuo, Zhang, Chi, Liu, Ying, Li, Shiming, Qi, Yue, Nathan, Arokia, Xu, Wenjun, Tang, Chenyu, Occhipinti, Edoardo, Yusufu, Mayinuer, Wang, Ningli, Bai, Weiling, Occhipinti, Luigi
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as hi
Externí odkaz:
http://arxiv.org/abs/2404.13388
Autor:
Wang, Jiaqi, Kang, Mengtian, Liu, Yong, Zhang, Chi, Liu, Ying, Li, Shiming, Qi, Yue, Xu, Wenjun, Tang, Chenyu, Occhipinti, Edoardo, Yusufu, Mayinuer, Wang, Ningli, Bai, Weiling, Gao, Shuo, Occhipinti, Luigi G.
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results. However, current methods are commonly based on supervise
Externí odkaz:
http://arxiv.org/abs/2404.13386
Autor:
Pang, Taotian, Lou, Xingyu, Zhao, Fei, Wu, Zhen, Dong, Kuiyao, Peng, Qiuying, Qi, Yue, Dai, Xinyu
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-grained user preferences and fu
Externí odkaz:
http://arxiv.org/abs/2403.16037
Autor:
Efaw, Corey M., Wu, Qisheng, Gao, Ningshengjie, Zhang, Yugang, Zhou, Haoyu, Gering, Kevin, Hurley, Michael F., Xiong, Hui, Hu, Enyuan, Cao, Xia, Xu, Wu, Zhang, Ji-Guang, Dufek, Eric J., Xiao, Jie, Yang, Xiao-Qing, Liu, Jun, Qi, Yue, Li, Bin
Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media despite having complex chemical composition and atomistic solvation structures, leaving a knowledge gap of microstructural characteristics. He
Externí odkaz:
http://arxiv.org/abs/2308.06910
Autor:
Jia-Bin Wang, Tong-Xing Lin, Deng-Hui Fan, You-Xin Gao, Yu-Jing Chen, Yu-Kai Wu, Kai-Xiang Xu, Qing-zhu Qiu, Ping Li, Jian-Wei Xie, Jian-Xian Lin, Qi-Yue Chen, Long-Long Cao, Chang-Ming Huang, Chao-Hui Zheng
Publikováno v:
Cancer Cell International, Vol 24, Iss 1, Pp 1-21 (2024)
Abstract Background Cancer stem cells (CSCs) are critical factors that limit the effectiveness of gastric cancer (GC) therapy. Circular RNAs (circRNAs) are confirmed as important regulators of many cancers. However, their role in regulating CSC-like
Externí odkaz:
https://doaj.org/article/87e775d3a8354c09ba09afd5d160280e
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Zhu, Huabin, Tan, Yanchao, Wang, Jun, Qi, Yue
Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the clas
Externí odkaz:
http://arxiv.org/abs/2307.14384