18 citations · 106 across the 28 of their papers we have counts for
5 papers · 1 filter
DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
Erfei Cui, Wenhai Wang, Zhiqi Li +7
Large language models (LLMs) have opened up new possibilities for intelligent agents, endowing them with human-like thinking and cognitive abilities. In this work, we delve into th…
Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Hao Li, Xue Yang, Zhaokai Wang +7
Many reinforcement learning environments (e.g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values. The challenge in exploration eff…
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
Linyan Huang, Zhiqi Li, Chonghao Sima +4
Current research is primarily dedicated to advancing the accuracy of camera-only 3D object detectors (apprentice) through the knowledge transferred from LiDAR- or multi-modal-based…
Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models
Zeqiang Lai, Xizhou Zhu, Jifeng Dai +2
The revolution of artificial intelligence content generation has been rapidly accelerated with the booming text-to-image (T2I) diffusion models. Within just two years of developmen…
ControlLLM: Augment Language Models with Tools by Searching on Graphs
Zhaoyang Liu, Zeqiang Lai, Zhangwei Gao +8
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable perform…