no code implementations • 16 Apr 2024 • Yuning Wang, Zhiyuan Liu, Haotian Lin, Junkai Jiang, Shaobing Xu, Jianqiang Wang
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
no code implementations • 16 Mar 2024 • Zhongqi Yang, Yuning Wang, Ken S. Yamashita, Maryam Sabah, Elahe Khatibi, Iman Azimi, Nikil Dutt, Jessica L. Borelli, Amir M. Rahmani
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial.
no code implementations • 24 Aug 2023 • Marcial Sanchis-Agudo, Yuning Wang, Roger Arnau, Luca Guastoni, Jasmin Lim, Karthik Duraisamy, Ricardo Vinuesa
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction.
no code implementations • 8 Aug 2023 • Yikun Liu, Yuning Wang, Cheng Liu
Accurate detection of natural deterioration and man-made damage on the surfaces of ancient stele in the first instance is essential for their preventive conservation.
no code implementations • 29 Jun 2023 • Yuning Wang, Zeyu Han, Yining Xing, Shaobing Xu, Jianqiang Wang
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving.
no code implementations • 25 Jun 2023 • Yuning Wang, Pu Zhang, Lei Bai, Jianru Xue
Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents.
no code implementations • 7 Apr 2023 • Alberto Solera-Rico, Carlos Sanmiguel Vila, M. A. Gómez, Yuning Wang, Abdulrahman Almashjary, Scott T. M. Dawson, Ricardo Vinuesa
Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows.
no code implementations • CVPR 2023 • Yuning Wang, Pu Zhang, Lei Bai, Jianru Xue
In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction.
no code implementations • 29 Mar 2022 • Hamidreza Eivazi, Yuning Wang, Ricardo Vinuesa
High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy.