Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing them with immense potential across a range of applications. However, in the field of computer vision, despite the availability of numerous powerful vision foundation models (VFMs), they are still restricted to tasks in a pre-defined form, struggling to match the open-ended task capabilities of LLMs. In this work, we present an LLM-based framework for vision-centric tasks, termed VisionLLM. This framework provides a unified perspective for vision and language tasks by treating images as a foreign language and aligning vision-centric tasks with language tasks that can be flexibly defined and managed using language instructions. An LLM-based decoder can then make appropriate predictions based on these instructions for open-ended tasks. Extensive experiments show that the proposed VisionLLM can achieve different levels of task customization through language instructions, from fine-grained object-level to coarse-grained task-level customization, all with good results. It's noteworthy that, with a generalist LLM-based framework, our model can achieve over 60\% mAP on COCO, on par with detection-specific models. We hope this model can set a new baseline for generalist vision and language models. The demo shall be released based on https://github.com/OpenGVLab/InternGPT. The code shall be released at https://github.com/OpenGVLab/VisionLLM.

翻译：大型语言模型凭借其针对用户定制任务的出色零样本能力，显著加速了迈向通用人工智能的进程，在众多应用中展现出巨大潜力。然而在计算机视觉领域，尽管已有多种强大的视觉基础模型，但它们仍局限于预定义形式的任务，难以匹敌大型语言模型的开放式任务处理能力。本研究提出一种基于大型语言模型的视觉中心任务框架——VisionLLM。该框架通过将图像视为一种"外语"，并将视觉中心任务与可用语言指令灵活定义和管理的语言任务对齐，为视觉与语言任务提供了统一视角。基于该框架，一个大型语言模型解码器能依据这些指令对开放式任务做出合理预测。大量实验表明，所提出的VisionLLM可通过语言指令实现从细粒度目标级到粗粒度任务级的多层次任务定制，且均取得优异效果。值得注意的是，基于通用型大型语言模型框架，本模型在COCO数据集上可达到超过60%的平均精度，与专用检测模型性能相当。我们期望本模型能为通用型视觉与语言模型树立新基准。演示将基于https://github.com/OpenGVLab/InternGPT发布，代码将于https://github.com/OpenGVLab/VisionLLM开源。