Abstract Visual Question Answering (VQA) is a significant cross-disciplinary issue in the
fields of computer vision and natural language processing that requires a computer to output …

This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Abstract The Visual Question Answering (VQA) system is the process of finding useful
information from images related to the question to answer the question correctly. It can be …

Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

Attention is an important mechanism that can be employed for a variety of deep learning
models across many different domains and tasks. This survey provides an overview of the …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Attention mechanism has demonstrated great potential in fine-grained visual recognition
tasks. In this paper, we present a counterfactual attention learning method to learn more …

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task
in computational pathology that involves modeling complex interactions within the tumor …

We study on joint learning of Convolutional Neural Network (CNN) and Transformer for
vision-language pre-training (VLPT) which aims to learn cross-modal alignments from …

Learning medical visual representations directly from paired radiology reports has become
an emerging topic in representation learning. However, existing medical image-text joint …