The history of text can be traced back over thousands of years. Rich and precise semantic
information carried by text is important in a wide range of vision-based application …

Abstract Medical Visual Question Answering (VQA) is a combination of medical artificial
intelligence and popular VQA challenges. Given a medical image and a clinically relevant …

Abstract Large Multimodal Models (LMMs) have shown promise in vision-language tasks but
struggle with high-resolution input and detailed scene understanding. Addressing these …

The exponential growth of large language models (LLMs) has opened up numerous
possibilities for multi-modal AGI systems. However the progress in vision and vision …

We present a new dataset for Visual Question Answering (VQA) on document images called
DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images …

Large models have recently played a dominant role in natural language processing and
multimodal vision-language learning. However, their effectiveness in text-related visual …

We propose a novel multimodal architecture for Scene Text Visual Question Answering
(STVQA), named Layout-Aware Transformer (LaTr). The task of STVQA requires models to …

In this paper, we propose Text-Aware Pre-training (TAP) for Text-VQA and Text-Caption
tasks. These two tasks aim at reading and understanding scene text in images for question …

A crucial component for the scene text based reasoning required for TextVQA and TextCaps
datasets involve detecting and recognizing text present in the images using an optical …

State-of-the-art scene text detection methods usually model the text instance with local
pixels or components from the bottom-up perspective and, therefore, are sensitive to noises …