TY - JOUR
AU - Dai, Jifeng
AB - Abstract: 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 this https URL. The code shall be released at this https URL.
TI - VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
JF - Computing Research Repository
DO - 10.48550/arxiv.2305.11175
DA - 2023-05-18
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/visionllm-large-language-model-is-also-an-open-ended-decoder-for-p2Mn6SP5Qj
VL - 2023
IS - 2305
DP - DeepDyve
ER -