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 …

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract
reasoning ability is the goal of next-generation AI. Recent advancements in Large Language …

Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …

Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …

We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision
experts to achieve multimodal reasoning and action. In this paper, we define and explore a …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Recent studies have shown promising results on utilizing large pre-trained image-language
models for video question answering. While these image-language models can efficiently …

The combination of strong visual backbones and Large Language Model (LLM) reasoning
has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range …

Despite recent advances in text-to-3D generative methods there is a notable absence of
reliable evaluation metrics. Existing metrics usually focus on a single criterion each such as …

Video question answering (VideoQA) is a complex task that requires diverse multi-modal
data for training. Manual annotation of question and answers for videos, however, is tedious …