8 citations · 21 across the 16 of their papers we have counts for
17 papers
AdaField: Generalizable Surface Pressure Modeling with Physics-Informed Pre-training and Flow-Conditioned Adaptation
Junhong Zou, Wei Qiu, Zhenxu Sun +3
The surface pressure field of transportation systems, including cars, trains, and aircraft, is critical for aerodynamic analysis and design. In recent years, deep neural networks h…
UniField: Joint Multi-Domain Training for Universal Surface Pressure Modeling
Junhong Zou, Zhenxu Sun, Yueqing Wang +4
Accurate modeling of surface pressure fields around objects is fundamental to aerodynamic analysis and design. While neural networks have shown promise as efficient alternatives to…
Improving Large Vision-Language Models' Understanding for Flow Field Data
Xiaomei Zhang, Hanyu Zheng, Xiangyu Zhu +4
Large Vision-Language Models (LVLMs) have shown impressive capabilities across a range of tasks that integrate visual and textual understanding, such as image captioning and visual…
Generating on Generated: An Approach Towards Self-Evolving Diffusion Models
Xulu Zhang, Xiaoyong Wei, Jinlin Wu +4
Recursive Self-Improvement (RSI) enables intelligence systems to autonomously refine their capabilities. This paper explores the application of RSI in text-to-image diffusion model…
Revisiting Marr in Face: The Building of 2D--2.5D--3D Representations in Deep Neural Networks
Xiangyu Zhu, Chang Yu, Jiankuo Zhao +3
David Marr's seminal theory of vision proposes that the human visual system operates through a sequence of three stages, known as the 2D sketch, the 2.5D sketch, and the 3D model.…
General Geometry-aware Weakly Supervised 3D Object Detection
Guowen Zhang, Junsong Fan, Liyi Chen +3
3D object detection is an indispensable component for scene understanding. However, the annotation of large-scale 3D datasets requires significant human effort. To tackle this prob…