4 papers
DINO-R1: Incentivizing Reasoning Capability in Vision Foundation Models
Chenbin Pan, Wenbin He, Zhengzhong Tu +1
The recent explosive interest in the reasoning capabilities of large language models, such as DeepSeek-R1, has demonstrated remarkable success through reinforcement learning-based…
AdaWM: Adaptive World Model based Planning for Autonomous Driving
Hang Wang, Xin Ye, Feng Tao +5
World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning polic…
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow
Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar +1
Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in int…
VLP: Vision Language Planning for Autonomous Driving
Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti +4
Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning. While vision-only autonomous driving methods have…