Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Visually-situated language is ubiquitous—sources range from textbooks with diagrams to
web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to …

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 …

Large Vision-Language Models (LVLMs) have recently played a dominant role in
multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation …

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 …

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.) …

To accelerate biomedical research process, deep-learning systems are developed to
automatically acquire knowledge about molecule entities by reading large-scale biomedical …

Structured document understanding has attracted considerable attention and made
significant progress recently, owing to its crucial role in intelligent document processing …