Training a Large Visual Language Model (LVLM) from scratch, like GPT-4, is
resource-intensive. Our paper proposes an alternative method called LMEye, a
play-plug-in Interactive Perception Network for Large Language Models (LLMs),
aiming to improve the accuracy of image understanding for the LVLM. Previous
methods that infuse visual information into LLMs utilize a static visual
mapping network, but lack dynamic interaction between the LLMs and visual
information. LMEye addresses this issue by allowing the LLM to incorporate the
visual information that aligned with human instruction. Specifically, the LMEye
network consists of a static visual mapping network to provide the basic
perception of an image to LLMs. Then, it also contains additional linear layers
responsible for acquiring requests from LLMs, decomposing image features, and
transmitting the interleaved information to LLMs, respectively. In this way,
LLMs act to be in charge of understanding human instructions, sending it to the
interactive perception network, and generating the response based on the
interleaved multimodal information. We evaluate LMEye through extensive
experiments on multimodal question answering and reasoning tasks, demonstrating
that it significantly improves the zero-shot performance of LLMs on multimodal
tasks compared to previous methods.

本文提出了一种名为 LMEye 的交互感知网络，旨在提高 Large Visual Language Model 的图像理解精度。LMEye 网络包括一个静态视觉映射网络和一些负责获取请求、分解图像特征和传输交错信息的线性层。通过在多模态问答和推理任务上进行广泛实验，我们证明 LMEye 显著提高了 LLMs 在多模态任务上的零 - shot 性能。