3 citations · 3 across the 9 of their papers we have counts for
12 papers
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…
AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents
Cunhao Zhu, Yifeng Wang, Dongliang Xu +3
World Action Models (WAMs) are becoming increasingly important and useful for embodied intelligence, as they enable robots to anticipate the consequences of candidate actions befor…
SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
Yue Yao, Shengyuan Wang, Xin Chen +4
Large language model agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not merely to retrieve individually…
DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement
Cunhao Zhu, Xiangtao Kong, Dongliang Xu +3
Underwater image enhancement is challenged by spatially non-uniform, wavelength-dependent attenuation. Propagation distance and wavelength govern this degradation, while YCbCr sepa…
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…
Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization
Zhixin Lin, Jungang Li, Dongliang Xu +5
Mobile GUI agents powered by Multimodal Large Language Models (MLLMs) can execute complex tasks on mobile devices. Despite this progress, most existing systems still optimize task…
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…
Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered Smartphone Agents
Zhixin Lin, Jungang Li, Shidong Pan +3
Smartphones bring significant convenience to users but also enable devices to extensively record various types of personal information. Existing smartphone agents powered by Multim…
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…