Multimodal large language models (MLLMs) have attracted considerable attention due to their exceptional performance in visual content understanding and reasoning. However, their inference efficiency has been a notable concern, as the increasing length of multimodal contexts leads to quadratic complexity. Token compression techniques, which reduce the number of visual tokens, have demonstrated their effectiveness in reducing computational costs. Yet, these approaches have struggled to keep pace with the rapid advancements in MLLMs, especially the AnyRes strategy in the context of high-resolution image understanding. In this paper, we propose a novel token compression method, GlobalCom$^2$, tailored for high-resolution MLLMs that receive both the thumbnail and multiple crops. GlobalCom$^2$ treats the tokens derived from the thumbnail as the ``commander'' of the entire token compression process, directing the allocation of retention ratios and the specific compression for each crop. In this way, redundant tokens are eliminated while important local details are adaptively preserved to the highest extent feasible. Empirical results across 10 benchmarks reveal that GlobalCom$^2$ achieves an optimal balance between performance and efficiency, and consistently outperforms state-of-the-art token compression methods with LLaVA-NeXT-7B/13B models. Our code is released at \url{https://github.com/xuyang-liu16/GlobalCom2}.

本研究针对多模态大语言模型（MLLMs）在推理效率上的挑战，提出了一种新型的令牌压缩方法GlobalCom$^2$，该方法专为处理高分辨率图像的MLLMs设计。通过将缩略图中提取的令牌视为整个压缩过程的“指挥官”，GlobalCom$^2$能够有效优化保留比率，消除冗余令牌并适应性地保留重要细节，实验结果显示其在性能与效率之间达到最佳平衡，超越了现有的压缩方法。

全球指导的压缩：无训练高分辨率多模态大语言模型加速