After more than 70 years of evolution, great achievements have been made in machine
translation. Especially in recent years, translation quality has been greatly improved with the …

In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This
has been propelled by the groundbreaking capabilities of generative models both in …

Self-supervised pre-training techniques have achieved remarkable progress in Document
AI. Most multimodal pre-trained models use a masked language modeling objective to learn …

Understanding document images (eg, invoices) is a core but challenging task since it
requires complex functions such as reading text and a holistic understanding of the …

In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a
Transformer-based image classifier equipped with co-scale and conv-attentional …

Plain text has become a prevalent interface for text-to-image synthesis. However, its limited
customization options hinder users from accurately describing desired outputs. For example …

Pre-training of text and layout has proved effective in a variety of visually-rich document
understanding tasks due to its effective model architecture and the advantage of large-scale …

We present DocFormer-a multi-modal transformer based architecture for the task of Visual
Document Understanding (VDU). VDU is a challenging problem which aims to understand …

Abstract We propose Universal Document Processing (UDOP), a foundation Document AI
model which unifies text, image, and layout modalities together with varied task formats …

Image Transformer has recently achieved significant progress for natural image
understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) …