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.
Tips:
ViLT is a model that takes both pixel_values and input_ids as input. One can use [ViltProcessor] to prepare data for the model.
  This processor wraps a feature extractor (for the image modality) and a tokenizer (for the language modality) into one.
