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.
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bibtex
1@article{guo2024dataefficient,
2 title={Data-efficient Large Vision Models through Sequential Autoregression},
3 author={Guo, Jianyuan and Hao, Zhiwei and Wang, Chengcheng and Tang, Yehui and Wu, Han and Hu, Han and Han, Kai and Xu, Chang},
4 journal={arXiv preprint arXiv:2402.04841},
5 year={2024}
6}
