Data-efficient Large Vision Models through Sequential Autoregression

Understand

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.