Multimodal Large Language Models (MLLMs) have experienced significant
advancements recently. Nevertheless, challenges persist in the accurate
recognition and comprehension of intricate details within high-resolution
images. Despite being indispensable for the development of robust MLLMs, this
area remains underinvestigated. To tackle this challenge, our work introduces
InfiMM-HD, a novel architecture specifically designed for processing images of
different resolutions with low computational overhead. This innovation
facilitates the enlargement of MLLMs to higher-resolution capabilities.
InfiMM-HD incorporates a cross-attention module and visual windows to reduce
computation costs. By integrating this architectural design with a four-stage
training pipeline, our model attains improved visual perception efficiently and
cost-effectively. Empirical study underscores the robustness and effectiveness
of InfiMM-HD, opening new avenues for exploration in related areas. Codes and
models can be found at this https URL

为了解决多模态大型语言模型在处理高分辨率图像时的准确识别和理解复杂细节方面的挑战，我们提出了 InfiMM-HD，一种专门设计用于处理不同分辨率图像的创新架构，能够提高模型的视觉感知能力，同时降低计算成本。经验证明 InfiMM-HD 具有鲁棒性和高效性，为相关领域的研究提供了新的探索途径。