  Vision-and-Language Pretraining (VLP) has improved performance on various
joint vision-and-language downstream tasks. Current approaches for VLP heavily
rely on image feature extraction processes, most of which involve region
supervisions (e.g., object detection) and the convolutional architecture (e.g.,
ResNet). Although disregarded in the literature, we find it problematic in
terms of both (1) efficiency/speed, that simply extracting input features
requires much more computation than the actual multimodal interaction steps;
and (2) expressive power, as it is upper bounded to the expressive power of the
visual encoder and its predefined visual vocabulary. In this paper, we present
a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the
sense that processing of visual inputs is drastically simplified to just the
same convolution-free manner that we process textual inputs. We show that ViLT
is up to 60 times faster than previous VLP models, yet with competitive or
better downstream task performance.
