3 papers
cs.CV2026
ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving
Dingyi Yao, Xinqi Zhang, Lihui Peng +3
Synthetic data mitigates the data scarcity problem in autonomous driving perception. However, the synthetic-to-real gap leads to performance degradation, hindering real-world model…
cs.CV2025
A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets
Dingyi Yao, Xinyao Han, Ruibo Ming +5
Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets ha…
cs.CV2024
ARCON: Advancing Auto-Regressive Continuation for Driving Videos
Ruibo Ming, Jingwei Wu, Zhewei Huang +4
Recent advancements in auto-regressive large language models (LLMs) have led to their application in video generation. This paper explores the use of Large Vision Models (LVMs) for…