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 the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

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 …

Recently leveraging large language models (LLMs) or multimodal large language models
(MLLMs) for document understanding has been proven very promising. However previous …

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 …

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

Document understanding refers to automatically extract, analyze and comprehend
information from various types of digital documents, such as a web page. Existing Multi …

We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our
approach introduces enhancement across several dimensions: By adopting Shifted Window …

Scientific knowledge is predominantly stored in books and scientific journals, often in the
form of PDFs. However, the PDF format leads to a loss of semantic information, particularly …