  Vision-and-Language Pre-training (VLP) has improved performance on various
joint vision-and-language downstream tasks. Current approaches to VLP heavily
rely on image feature extraction processes, most of which involve region
supervision (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 multimodal interaction steps; and (2)
expressive power, as it is upper bounded to the expressive power of the visual
embedder and its predefined visual vocabulary. In this paper, we present a
minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the
sense that the 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 tens of times faster than previous VLP models, yet with
competitive or better downstream task performance. Our code and pre-trained
weights are available at https://github.com/dandelin/vilt.
