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Multimodal Deep Learning

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arxiv 2301.04856 v1 pith:DDZN4VY2 submitted 2023-01-12 cs.CL cs.LGstat.ML

Cem Akkus , Luyang Chu , Vladana Djakovic , Steffen Jauch-Walser , Philipp Koch , Giacomo Loss , Christopher Marquardt , Marco Moldovan
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Nadja Sauter Maximilian Schneider Rickmer Schulte Karol Urbanczyk Jann Goschenhofer Christian Heumann Rasmus Hvingelby Daniel Schalk Matthias A{ss}enmacher
This is my paper · ORCID
classification cs.CLcs.LGstat.ML
keywords learningmodalitiesotherapproachesdeepdifferentmodalitymodels
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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.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 9 citations worldwide. Full citation record

  1. FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data

    cs.LG 2026-08 conditional novelty 6.0 of 10

    FiGuRO estimates the intrinsic dimensionality of shared and private subspaces in multi-modal data by adaptively growing or shrinking low-rank bottleneck layers guided by a reconstruction-fidelity budget.

  2. NanoVLMs: How small can we go and still make coherent Vision Language Models?

    cs.CV 2025-02 reject novelty 5.0 of 10

    NanoVLMs, 5M to 25M parameter vision-language models trained on simplified GPT-4o captions, are judged by GPT-4o as nearly as coherent as the 50x larger Kosmos-2 on a 25-sample test.

  3. Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services

    cs.DC 2024-12 conditional novelty 1.0 of 10

    A scoping review that aggregates and compares major cloud providers' generative AI tools and services, with no new empirical results.

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