Zobrazeno 1 - 10
of 183
pro vyhledávání: '"Xu, Zelin"'
Traffic forecasting uses recent measurements by sensors installed at chosen locations to forecast the future road traffic. Existing work either assumes all locations are equipped with sensors or focuses on short-term forecast. This paper studies part
Externí odkaz:
http://arxiv.org/abs/2408.02689
Autor:
Xiao, Tingsong, Xu, Zelin, He, Wenchong, Su, Jim, Zhang, Yupu, Opoku, Raymond, Ison, Ronald, Petho, Jason, Bian, Jiang, Tighe, Patrick, Rashidi, Parisa, Jiang, Zhe
Event prediction aims to forecast the time and type of a future event based on a historical event sequence. Despite its significance, several challenges exist, including the irregularity of time intervals between consecutive events, the existence of
Externí odkaz:
http://arxiv.org/abs/2402.02258
Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is often hindered by the limited training labels. Given Earth imagery with
Externí odkaz:
http://arxiv.org/abs/2312.07767
Autor:
He, Wenchong, Jiang, Zhe, Xiao, Tingsong, Xu, Zelin, Chen, Shigang, Fick, Ronald, Medina, Miles, Angelini, Christine
Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a million) point
Externí odkaz:
http://arxiv.org/abs/2311.04434
Domain gap between synthetic and real data in visual regression (e.g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imp
Externí odkaz:
http://arxiv.org/abs/2305.10808
Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make une
Externí odkaz:
http://arxiv.org/abs/2302.13425
Autor:
Wang, Tiqing, Zhu, Feng, Li, Peng, Xu, Zelin, Ma, Tingfeng, Kuznetsova, Iren, Dong, Bin, Qian, Zhenghua
Publikováno v:
Journal of Applied Physics; 9/21/2024, Vol. 136 Issue 11, p1-17, 17p
The challenges of learning a robust 6D pose function lie in 1) severe occlusion and 2) systematic noises in depth images. Inspired by the success of point-pair features, the goal of this paper is to recover the 6D pose of an object instance segmented
Externí odkaz:
http://arxiv.org/abs/2205.03536
Autor:
Xu, Zelin, Konno, Yoshihiro
Publikováno v:
In Marine and Petroleum Geology October 2024 168
Autor:
Huang, Jun, Lin, Yiyang, Fu, Yanqing, Xu, Zelin, Hong, Huilan, Arbing, Rachel, Chen, Wei-Ti, Wang, Anni, Huang, Feifei
Publikováno v:
In Midwifery December 2024 139