A survey of multi-modal learning theory

HUANG Yu; HUANG Longbo

doi:10.13471/j.cnki.acta.snus.2023A022

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A survey of multi-modal learning theory

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- A survey of multi-modal learning theory
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 62, Issue 5, Pages: 38-49(2023)
- 作者机构：
  
  Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
- 作者简介：
  
  [ " HUANG Yu（y-huang20@mails.tsinghua.edu.cn） " ]
  HUANG Longbo（longbohuang@tsinghua.edu.cn）；
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2023A022
  CLC： TP18
- Published：25 September 2023，
  
  Published Online：04 September 2023，
  
  Received：28 March 2023，
  
  Accepted：11 April 2023
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HUANG Yu,HUANG Longbo.A survey of multi-modal learning theory[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(05):38-49.
DOI：

HUANG Yu,HUANG Longbo.A survey of multi-modal learning theory[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(05):38-49. DOI： 10.13471/j.cnki.acta.snus.2023A022.

摘要

Abstract

Deep multi-modal learning

a rapidly growing field with a wide range of practical applications

aims to effectively utilize and integrate information from multiple sources

known as modalities. Despite its impressive empirical performance

the theoretical foundations of deep multi-modal learning have yet to be fully explored. In this paper

we will undertake a comprehensive survey of recent developments in multi-modal learning theories

focusing on the fundamental properties that govern this field. Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning

to clarify their implications for practitioners

and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.

关键词

Keywords

multi-modal learningmachine learning theoryoptimizationgeneralization

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