22 citations · 67 across the 24 of their papers we have counts for
6 papers · 1 filter
Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models
Meng Cao, Yuyang Liu, Yingfei Liu +6
Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language Models (LVLMs) to meet individual task requirements. To date, most of the existing approach…
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework…
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?
Yifan Bai, Dongming Wu, Yingfei Liu +8
Rapid advancements in Autonomous Driving (AD) tasks turned a significant shift toward end-to-end fashion, particularly in the utilization of vision-language models (VLMs) that inte…
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Binyuan Huang, Yuqing Wen, Yucheng Zhao +9
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data…
Stream Query Denoising for Vectorized HD Map Construction
Shuo Wang, Fan Jia, Yingfei Liu +6
To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particul…