Multimodal Machine Learning Survey

In recent times, multimodal machinelearning survey has become increasingly relevant in various contexts. A Survey of MultimodalLearning: Methods, Applications, and Future. In this article, we start with the form of a multimodal combination and provide a comprehensive survey of the emerging subject of multimodal machine learning, covering representative research approaches, the most recent advancements, and their applications. Multimodal Machine Learning: A Survey and Taxonomy. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. Self-Supervised Multimodal Learning: A Survey - IEEE Xplore.

Abstract: Multimodal learning, which aims to understand and analyze information from multiple modalities, has achieved substantial progress in the supervised regime in recent years. However, the heavy dependence on data paired with expensive human annotations impedes scaling up models. TL;DR: This paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy to enable researchers to better understand the state of the field and identify directions for future research. MechRAG: a multimodal large language model for mechanical ...

Shuang Li and colleague propose a multimodal, retrieval-augmented, large language model MechRAG. It integrates heterogeneous CAD/CAE digital assets into its responses to engineering questions ... [2411.17040] Multimodal Alignment and Fusion: A Survey.

This survey provides a comprehensive overview of recent advances in multimodal alignment and fusion within the field of machine learning, driven by the increasing availability and diversity of data modalities such as text, images, audio, and video.