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 …

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Abstract Knowledge-based visual question answering (VQA) requires external knowledge
beyond the image to answer the question. Early studies retrieve required knowledge from …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Abstract Knowledge-based visual question answering (VQA) involves answering questions
that require external knowledge not present in the image. Existing methods first retrieve …

Large language models (LLMs) have demonstrated excellent zero-shot generalization to
new language tasks. However, effective utilization of LLMs for zero-shot visual question …

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model
(REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve …

The primary focus of recent work with largescale transformers has been on optimizing the
amount of information packed into the model's parameters. In this work, we ask a different …

Foundation models have received much attention due to their effectiveness across a broad
range of downstream applications. Though there is a big convergence in terms of …

Knowledge-based visual question answering (VQA) involves questions that require world
knowledge beyond the image to yield the correct answer. Large language models (LMs) like …