Welcome to the MLLM Tutorial series on CVPR 2024!

Artificial intelligence (AI) encompasses knowledge acquisition and real-world grounding across various modalities. As a multidisciplinary research field, multimodal large language models (MLLMs) have recently garnered growing interest in both academia and industry, showing an unprecedented trend to achieve human-level AI via MLLMs. These large models offer an effective vehicle for understanding, reasoning, and planning by integrating and modeling diverse information modalities, including language, visual, auditory, and sensory data. This tutorial aims to deliver a comprehensive review of cutting-edge research in MLLMs, focusing on four key areas: MLLM architecture design, instructional learning&hallucination, multimodal reasoning of MLLMs and efficient learning in MLLMs. We will explore technical advancements, synthesize key challenges, and discuss potential avenues for future research.

Seattle local time zone (UTC-7): Tuesday, June 18, 1:30 PM-6:00 PM

Beijing time zone (UTC+8): Wednesday, June 19, 4:30 AM - 9:00 AM

@inproceedings{fei2024multimodal,
  title={From Multimodal LLM to Human-level AI: Modality, Instruction, Reasoning and Beyond},
  author={Fei, Hao and Li, Xiangtai and Liu, Haotian and Liu, Fuxiao and Zhang, Zhuosheng and Zhang, Hanwang and Yan, Shuicheng},
  booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
  pages={11289--11291},
  year={2024}
}