In the past year, Multimodal Large Language Models (MLLMs) have demonstrated
remarkable performance in tasks such as visual question answering, visual
understanding and reasoning. However, the extensive model size and high
training and inference costs have hindered the widespread application of MLLMs
in academia and industry. Thus, studying efficient and lightweight MLLMs has
enormous potential, especially in edge computing scenarios. In this survey, we
provide a comprehensive and systematic review of the current state of efficient
MLLMs. Specifically, we summarize the timeline of representative efficient
MLLMs, research state of efficient structures and strategies, and the
applications. Finally, we discuss the limitations of current efficient MLLM
research and promising future directions. Please refer to our GitHub repository
for more details:
this https URL

在这份调研报告中，我们综述了当前高效多模态大语言模型（MLLMs）的研究状况，包括代表性的高效 MLLM 的时间线、高效结构和策略的研究现状以及应用领域。最后，我们讨论了目前高效 MLLM 研究的限制和有前途的未来方向。