  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.
