Training general-purpose vision models on purely sequential visual data,
eschewing linguistic inputs, has heralded a new frontier in visual
understanding. These models are intended to not only comprehend but also
seamlessly transit to out-of-domain tasks. However, current endeavors are
hamstrung by an over-reliance on colossal models, exemplified by models with
upwards of 3B parameters, and the necessity for an extensive corpus of visual
data, often comprising a staggering 400B tokens. In this paper, we delve into
the development of an efficient, autoregression-based vision model,
innovatively architected to operate on a limited dataset. We meticulously
demonstrate how this model achieves proficiency in a spectrum of visual tasks
spanning both high-level and low-level semantic understanding during the
testing phase. Our empirical evaluations underscore the model's agility in
adapting to various tasks, heralding a significant reduction in the parameter
footprint, and a marked decrease in training data requirements, thereby paving
the way for more sustainable and accessible advancements in the field of
generalist vision models. The code is available at
this https URL

该论文研究了一种高效的基于自回归的视觉模型，通过有限的数据集在测试阶段展示了其在高层次和低层次语义理解等各种视觉任务上的能力，减少了参数占用以及训练数据要求，为通用视觉模型领域的可持续性和可访问性提供了重要进展。