VisionLLaMA: A Unified LLaMA Interface for Vision Tasks

Introduction

Large language models are built on top of a transformer-based architecture to process textual inputs. For example, the LLaMA stands out among many open-source implementations. Can the same transformer be used to process 2D images? In this paper, we answer this question by unveiling a LLaMA-like vision transformer in plain and pyramid forms, termed VisionLLaMA, which is tailored for this purpose. VisionLLaMA is a unified and generic modelling framework for solving most vision tasks. We extensively evaluate its effectiveness using typical pre-training paradigms in a good portion of downstream tasks of image perception and especially image generation. In many cases, VisionLLaMA have exhibited substantial gains over the previous state-of-the-art vision transformers. We believe that VisionLLaMA can serve as a strong new baseline model for vision generation and understanding.

Updates

Stay tuned, our code will be released soon!

✏️ Reference

If you find VisionLLaMA useful in your research or applications, please consider giving a star ⭐ and citing using the following BibTeX:

@article{chu2024visionllama,
  title={VisionLLaMA: A Unified LLaMA Interface for Vision Tasks},
  author={Chu, Xiangxiang and Su, Jianlin and Zhang, Bo and Shen, Chunhua},
  journal={arXiv preprint arXiv:2403.00522},
  year={2024}
}