5 citations · 5 across the 4 of their papers we have counts for
4 papers
The Better You Learn, The Smarter You Prune: Towards Efficient Vision-language-action Models via Differentiable Token Pruning
Titong Jiang, Xuefeng Jiang, Yuan Ma +7
We present LightVLA, a simple yet effective differentiable token pruning framework for vision-language-action (VLA) models. While VLA models have shown impressive capability in exe…
TokenFLEX: Unified VLM Training for Flexible Visual Tokens Inference
Junshan Hu, Jialiang Mao, Zhikang Liu +3
Conventional Vision-Language Models(VLMs) typically utilize a fixed number of vision tokens, regardless of task complexity. This one-size-fits-all strategy introduces notable ineff…
Generalizing Motion Planners with Mixture of Experts for Autonomous Driving
Qiao Sun, Huimin Wang, Jiahao Zhan +7
Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, mod…
PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
Yupeng Zheng, Zebin Xing, Qichao Zhang +8
Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios bu…