17 citations · 21 across the 13 of their papers we have counts for
13 papers
VeCAS: Vessel-Focused Contrast-Free Angiogram Synthesis for Vascular Interventions
De-Xing Huang, Chen-Yu Wang, Hao Liang +10
X-ray angiography relies on iodinated contrast agents to visualize vascular structures during image-guided interventions. However, contrast administration carries risks of adverse…
Backbone-Agnostic Stochastic Perturbation Learning for End-to-End Real-World Image Dehazing
Bingcai Wei, Yuning Cui, Mingyu Liu +5
Real-world paired image dehazing remains challenging because haze degradation is spatially non-uniform, illumination-dependent, and physically ambiguous even when haze-free referen…
CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs
Xingcheng Zhou, Hao Guo, Rui Song +5
Safety-critical traffic reasoning requires contrastive consistency: models must detect true hazards when an accident occurs, and reliably reject plausible-but-false hypotheses unde…
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +54
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration…
SGTA: Scene-Graph Based Multi-Modal Traffic Agent for Video Understanding
Xingcheng Zhou, Mingyu Liu, Walter Zimmer +2
We present Scene-Graph Based Multi-Modal Traffic Agent (SGTA), a modular framework for traffic video understanding that combines structured scene graphs with multi-modal reasoning.…
CAS-IQA: Teaching Vision-Language Models for Synthetic Angiography Quality Assessment
Bo Wang, De-Xing Huang, Xiao-Hu Zhou +5
Synthetic X-ray angiographies generated by modern generative models hold great potential to reduce the use of contrast agents in vascular interventional procedures. However, low-qu…
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Xingcheng Zhou, Konstantinos Larintzakis, Hao Guo +7
We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 vid…
WARM-3D: A Weakly-Supervised Sim2Real Domain Adaptation Framework for Roadside Monocular 3D Object Detection
Xingcheng Zhou, Deyu Fu, Walter Zimmer +4
Existing roadside perception systems are limited by the absence of publicly available, large-scale, high-quality 3D datasets. Exploring the use of cost-effective, extensive synthet…
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
Mingyu Liu, Ekim Yurtsever, Marc Brede +6
Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibit…
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou +2
In the context of Intelligent Transportation Systems (ITS), efficient data compression is crucial for managing large-scale point cloud data acquired by roadside LiDAR sensors. The…
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Mingyu Liu, Ekim Yurtsever, Jonathan Fossaert +5
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for…
3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark
Bare Luka Žagar, Tim Hertel, Mingyu Liu +2
Deformable linear objects are vastly represented in our everyday lives. It is often challenging even for humans to visually understand them, as the same object can be entangled so…
Vision Language Models in Autonomous Driving: A Survey and Outlook
Xingcheng Zhou, Mingyu Liu, Ekim Yurtsever +4
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability t…