7 citations · 12 across the 9 of their papers we have counts for
11 papers
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…
SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality
Chenyang Lei, Liyi Chen, Jun Cen +5
Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many d…
SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality
Chenyang Lei, Liyi Chen, Jun Cen +6
Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many d…
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…
ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation
Zhiyuan Ma, Yuxiang Wei, Yabin Zhang +3
By leveraging the text-to-image diffusion priors, score distillation can synthesize 3D contents without paired text-3D training data. Instead of spending hours of online optimizati…