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 We present Fast Language-Image Pre-training (FLIP), a simple and more efficient
method for training CLIP. Our method randomly masks out and removes a large portion of …

Top-down visual attention mechanisms have been used extensively in image captioning
and visual question answering (VQA) to enable deeper image understanding through fine …

We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …

Problems at the intersection of vision and language are of significant importance both as
challenging research questions and for the rich set of applications they enable. However …

Abstract Visual Question Answering (VQA) requires a fine-grained and simultaneous
understanding of both the visual content of images and the textual content of questions …

Attention networks in multimodal learning provide an efficient way to utilize given visual
information selectively. However, the computational cost to learn attention distributions for …

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

In this paper, a knowledge base graph embedding module is constructed to extend the
versatility of knowledge-based VQA (Visual Question Answering) models. The knowledge …