Multimodal Deep Learning

Cem Akkus, Luyang Chu, Vladana Djakovic, Steffen Jauch-Walser, Philipp Koch +12 more
1/12/2023
cs.CLcs.LGstat.ML

No official code found. Best implementation: slds-lmu/seminar_multimodal_dl (170 stars, last commit 2023-01-16).

Abstract

This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in which one modality is utilized to enhance representation learning for the other. To conclude the second part, architectures with a focus on handling both modalities simultaneously are introduced. Finally, we also cover other modalities as well as general-purpose multi-modal models, which are able to handle different tasks on different modalities within one unified architecture. One interesting application (Generative Art) eventually caps off this booklet.

View on arXivView PDF

Code Implementations(1)

slds-lmu/seminar_multimodal_dlCommunity100%

https://slds-lmu.github.io/seminar_multimodal_dl/

17027R, CSSMar 28, 20223 years ago

Similar Papers

Multimodal Representation Learning and Fusion

87% match2025

DeepSuM: Deep Sufficient Modality Learning Framework

86% match2025

MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning

85% match1 repo6352023

Generalist Multimodal AI: A Review of Architectures, Challenges and Opportunities

84% match2024

Towards LLM-Centric Multimodal Fusion: A Survey on Integration Strategies and Techniques

83% match2025

The Evolution of Multimodal Model Architectures

83% match2024

Cite this paper

@article{akkus2023multimodal,
  title  = {Multimodal Deep Learning},
  author = {Cem Akkus and Luyang Chu and Vladana Djakovic and Steffen Jauch-Walser and Philipp Koch and Giacomo Loss and Christopher Marquardt and Marco Moldovan and Nadja Sauter and Maximilian Schneider and Rickmer Schulte and Karol Urbanczyk and Jann Goschenhofer and Christian Heumann and Rasmus Hvingelby and Daniel Schalk and Matthias Aßenmacher},
  year   = {2023},
  eprint = {2301.04856},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.04856v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2301.04856.svg)](https://codeofpaper.com/paper/2301.04856)

Discussion