In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify
vision-language tasks such as image/video captioning and question answering. While …

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 …

Image and text matching plays a crucial role in bridging the cross-modal gap between vision
and language, and has achieved great progress due to the deep learning. However, the …

Text-based Visual Question Answering (TextVQA) aims to produce correct answers for given
questions about the images with multiple scene texts. In most cases, the texts naturally …

Referring video object segmentation aims to segment the object referred by a given
language expression. Existing works typically require compressed video bitstream to be …

There are already some text-based visual question answering (TextVQA) benchmarks for
developing machine's ability to answer questions based on texts in images in recent years …

Abstract Visual Question Answering (VQA) is a multi-disciplinary research problem that has
captured the attention of both computer vision as well as natural language processing …

Text-based visual question answering (TextVQA) requires analyzing both the visual contents
and texts in an image to answer a question, which is more practical than general visual …

Text-based visual question answering (TextVQA) faces the significant challenge of avoiding
redundant relational inference. To be specific, a large number of detected objects and …