VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Xiangxiang Chu Jianlin Su Bo Zhang Chunhua Shen
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. Our code will be released at
https://github.com/Meituan-AutoML/VisionLLaMA.