  Enormous waves of technological innovations over the past several years,
marked by the advances in AI technologies, are profoundly reshaping the
industry and the society. However, down the road, a key challenge awaits us,
that is, our capability of meeting rapidly-growing scenario-specific demands is
severely limited by the cost of acquiring a commensurate amount of training
data. This difficult situation is in essence due to limitations of the
mainstream learning paradigm: we need to train a new model for each new
scenario, based on a large quantity of well-annotated data and commonly from
scratch. In tackling this fundamental problem, we move beyond and develop a new
learning paradigm named INTERN. By learning with supervisory signals from
multiple sources in multiple stages, the model being trained will develop
strong generalizability. We evaluate our model on 26 well-known datasets that
cover four categories of tasks in computer vision. In most cases, our models,
adapted with only 10% of the training data in the target domain, outperform the
counterparts trained with the full set of data, often by a significant margin.
This is an important step towards a promising prospect where such a model with
general vision capability can dramatically reduce our reliance on data, thus
expediting the adoption of AI technologies. Furthermore, revolving around our
new paradigm, we also introduce a new data system, a new architecture, and a
new benchmark, which, together, form a general vision ecosystem to support its
future development in an open and inclusive manner. See project website at
https://opengvlab.shlab.org.cn .
