2 citations · 3 across the 3 of their papers we have counts for
4 papers
CtrlWAM: Controllable World Action Models with Aligned Intent and Foresight
Chensheng Peng, Wenhao Ding, Ran Tian +9
World action models (WAMs) jointly predict actions (intent) and visual future (foresight). Standard training adds noise to recorded actions and video simultaneously, but such train…
Latent Chain-of-Thought World Modeling for End-to-End Driving
Shuhan Tan, Kashyap Chitta, Yuxiao Chen +8
Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most…
Wolf: Dense Video Captioning with a World Summarization Framework
Boyi Li, Ligeng Zhu, Ran Tian +20
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging comp…
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
Ran Tian, Boyi Li, Xinshuo Weng +5
The autonomous driving industry is increasingly adopting end-to-end learning from sensory inputs to minimize human biases in system design. Traditional end-to-end driving models, h…