Zobrazeno 1 - 10
of 11 668
pro vyhledávání: '"Ronghui, An"'
In observational studies with delayed entry, causal inference for time-to-event outcomes can be challenging. The challenges arise because, in addition to the potential confounding bias from observational data, the collected data often also suffers fr
Externí odkaz:
http://arxiv.org/abs/2411.18879
Autor:
Han, Haonan, Wu, Xiangzuo, Liao, Huan, Xu, Zunnan, Hu, Zhongyuan, Li, Ronghui, Zhang, Yachao, Li, Xiu
Recently, text-to-motion models have opened new possibilities for creating realistic human motion with greater efficiency and flexibility. However, aligning motion generation with event-level textual descriptions presents unique challenges due to the
Externí odkaz:
http://arxiv.org/abs/2411.18654
Autor:
Xu, Ronghui, Cheng, Hanyin, Guo, Chenjuan, Gao, Hongfan, Hu, Jilin, Yang, Sean Bin, Yang, Bin
Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on
Externí odkaz:
http://arxiv.org/abs/2411.18428
Accurate time series forecasting, predicting future values based on past data, is crucial for diverse industries. Many current time series methods decompose time series into multiple sub-series, applying different model architectures and training wit
Externí odkaz:
http://arxiv.org/abs/2411.11340
Autor:
Li, Ronghui, Zhang, Hongwen, Zhang, Yachao, Zhang, Yuxiang, Zhang, Youliang, Guo, Jie, Zhang, Yan, Li, Xiu, Liu, Yebin
We propose Lodge++, a choreography framework to generate high-quality, ultra-long, and vivid dances given the music and desired genre. To handle the challenges in computational efficiency, the learning of complex and vivid global choreography pattern
Externí odkaz:
http://arxiv.org/abs/2410.20389
Autor:
Tian, Jindong, Liang, Yuxuan, Xu, Ronghui, Chen, Peng, Guo, Chenjuan, Zhou, Aoying, Pan, Lujia, Rao, Zhongwen, Yang, Bin
Air pollution significantly threatens human health and ecosystems, necessitating effective air quality prediction to inform public policy. Traditional approaches are generally categorized into physics-based and data-driven models. Physics-based model
Externí odkaz:
http://arxiv.org/abs/2410.19892
With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to mainta
Externí odkaz:
http://arxiv.org/abs/2410.18007
Autor:
Chen, Junzhou, Huang, Heqiang, Zhang, Ronghui, Lyu, Nengchao, Guo, Yanyong, Dai, Hong-Ning, Yan, Hong
Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, they often compromise between sp
Externí odkaz:
http://arxiv.org/abs/2410.17144
Autor:
Zhang, Ronghui, Yang, Shangyu, Lyu, Dakang, Wang, Zihan, Chen, Junzhou, Ren, Yilong, Gao, Bolin, Lv, Zhihan
Road ponding, a prevalent traffic hazard, poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies struggle to accurately identify
Externí odkaz:
http://arxiv.org/abs/2410.16999
Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability to estimate uncertainty of imputation results. Meanwhile, denoising diffusion probabilistic models (DDPMs) have achieved great success in probabil
Externí odkaz:
http://arxiv.org/abs/2410.13338