impacts limitations

Broader Impacts NaViT enables training of vision transformers on variable size inputs, which has a profound impact on advancing adaptive computation research. By training models to handle various input size, we can explore adaptive computation techniques that dynamically adjust the computational resources based on the specific requirements of a given input. This flexibility opens up new avenues for implementing ideas that aim at adjusting allocation of compute and improving efficiency in vision tasks per input. Furthermore, NaViT computational efficiency unlocks the potential for scaling up pre-training of vision models. With the ability to handle different resolutions, models can effectively tackle more complex and diverse visual data, allowing for the development of larger and more powerful vision models.