Ma, Xiaojian

MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

Zhao, H., Cai, Z., Si, S., Ma, X., An, K., Chen, L., Liu, Z., Wang, S., Han, W., & Chang, B. (2023) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 120

Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

Wang, Z., Cai, S., Chen, G., Liu, A., Ma, X., & Liang, Y. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 55

Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

Cai, S., Wang, Z., Ma, X., Liu, A., & Liang, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 12

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Wang, Z., Cai, S., Liu, A., Jin, Y., Hou, J., Zhang, B., Lin, H., He, Z., Zheng, Z., Yang, Y., et al. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 6

MindAgent: Emergent Gaming Interaction

Gong, R., Huang, Q., Ma, X., Vo, H., Durante, Z., Noda, Y., Zheng, Z., Zhu, S.-C., Terzopoulos, D., Fei-Fei, L., et al. (2023) · arXiv

Artificial Intelligence in GamesMulti-Agent Systems and Negotiation

Cited by 1