3 citations · 3 across the 9 of their papers we have counts for
7 papers · 1 filter
PIC-UIE: Predicting Image-Adaptive Corrections for Lightweight Underwater Image Enhancement
Cunhao Zhu, Dongliang Xu, Xiangtao Kong +3
Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenuation and backscatter. State…
No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
Feinan Cheng, Dongliang Xu, Wenli Nong +4
Test-time scaling offers a promising method to improve the inference performance of Vision-Language Models (VLMs) without additional training. Existing approaches to vision-languag…
Solve the Missing First Step: Can VLMs Standardize Raw Heterogeneous Medical Data?
Xin Chen, Dongliang Xu, Cunhao Zhu +5
As vision-language models (VLMs) are increasingly applied to medical AI, existing benchmarks mainly focus on evaluating their diagnostic ability over given medical images and texts…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
From Static to Dynamic: a Survey of Topology-Aware Perception in Autonomous Driving
Yixiao Chen, Ruining Yang, Xin Chen +3
The key to achieving autonomous driving lies in topology-aware perception, the structured understanding of the driving environment with an emphasis on lane topology and road semant…
Are VLMs Ready for Lane Topology Awareness in Autonomous Driving?
Xin Chen, Jia He, Maozheng Li +5
Vision-Language Models (VLMs) have recently shown remarkable progress in multimodal reasoning, yet their applications in autonomous driving remain limited. In particular, the abili…
Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs
Yue Yao, Zelin Wen, Yan Tong +5
Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. In this paper, we explore a simpl…