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…
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation
Pengfei Wang, Yuxi Wang, Shuai Li +3
The scarcity of large-scale 3D-text paired data poses a great challenge on open vocabulary 3D scene understanding, and hence it is popular to leverage internet-scale 2D data and tr…
Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection
Guowen Zhang, Lue Fan, Chenhang He +3
Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D obje…
FIRM: Flexible Interactive Reflection reMoval
Xiao Chen, Xudong Jiang, Yunkang Tao +4
Removing reflection from a single image is challenging due to the absence of general reflection priors. Although existing methods incorporate extensive user guidance for satisfacto…
Top-Down Guidance for Learning Object-Centric Representations
Junhong Zou, Xiangyu Zhu, Zhaoxiang Zhang +1
Humans' innate ability to decompose scenes into objects allows for efficient understanding, predicting, and planning. In light of this, Object-Centric Learning (OCL) attempts to en…
A Survey on Personalized Content Synthesis with Diffusion Models
Xulu Zhang, Xiaoyong Wei, Wentao Hu +6
Recent advancements in diffusion models have significantly impacted content creation, leading to the emergence of Personalized Content Synthesis (PCS). By utilizing a small set of…
Generative Active Learning for Image Synthesis Personalization
Xulu Zhang, Wengyu Zhang, Xiao-Yong Wei +4
This paper presents a pilot study that explores the application of active learning, traditionally studied in the context of discriminative models, to generative models. We specific…
DiffSpeaker: Speech-Driven 3D Facial Animation with Diffusion Transformer
Zhiyuan Ma, Xiangyu Zhu, Guojun Qi +3
Speech-driven 3D facial animation is important for many multimedia applications. Recent work has shown promise in using either Diffusion models or Transformer architectures for thi…
Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering
Chang Yu, Junran Peng, Xiangyu Zhu +3
The text-to-image synthesis by diffusion models has recently shown remarkable performance in generating high-quality images. Although performs well for simple texts, the models may…
Compositional Inversion for Stable Diffusion Models
Xulu Zhang, Xiao-Yong Wei, Jinlin Wu +4
Inversion methods, such as Textual Inversion, generate personalized images by incorporating concepts of interest provided by user images. However, existing methods often suffer fro…
GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives
Zuyao Chen, Jinlin Wu, Zhen Lei +2
Training Scene Graph Generation (SGG) models with natural language captions has become increasingly popular due to the abundant, cost-effective, and open-world generalization super…
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention
Zuyao Chen, Jinlin Wu, Zhen Lei +2
Scene Graph Generation (SGG) offers a structured representation critical in many computer vision applications. Traditional SGG approaches, however, are limited by a closed-set assu…