Multimodal representation learning: Difference between revisions

Revision as of 21:24, 6 July 2025

Latest revision as of 23:40, 26 August 2025

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The primary motivations for multimodal representation learning arise from the inherent nature of real-world data and the limitations of unimodal approaches. Since multimodal data offers complementary and supplementary information about an object or event from different perspectives, it is more informative than relying on a single modality.<ref name=":0" /> A key motivation is to narrow the heterogeneity gap that exists between different modalities by projecting their features into a shared semantic subspace. This allows semantically similar content across modalities to be represented by similar vectors, facilitating the understanding of relationships and correlations between them. Multimodal representation learning aims to leverage the unique information provided by each modality to achieve a more comprehensive and accurate understanding of concepts.

These unified representations are crucial for improving performance in various cross-media analysis tasks such as video classification, event detection, and sentiment analysis. They also enable cross-modal retrieval, allowing users to search and retrieve content across different modalities.<ref>{{Cite book |last1=Zhang |first1=Su-Fang |last2=Zhai |first2=Jun-Hai |last3=Xie |first3=Bo-Jun |last4=Zhan |first4=Yan |last5=Wang |first5=Xin |chapter=Multimodal Representation Learning: Advances, Trends and Challenges |date=July 2019 |title=2019 International Conference on Machine Learning and Cybernetics (ICMLC) |chapter-url=https://ieeexplore.ieee.org/document/8949228 |publisher=IEEE |pages=1–6 |doi=10.1109/ICMLC48188.2019.8949228 |isbn=978-1-7281-2816-0}}</ref> Additionally, it facilitates cross-modal translation, where information can be converted from one modality to another, as seen in applications like image captioning and text-to-image synthesis. The abundance of ubiquitous multimodal data in real-world applications, including understudied areas like healthcare, finance, and human-computer interaction (HCI), further motivates the development of effective multimodal representation learning techniques.<ref>{{Cite journal |last1=Zhang |first1=Chao |last2=Yang |first2=Zichao |last3=He |first3=Xiaodong |last4=Deng |first4=Li |date=March 2020 |title=Multimodal Intelligence: Representation Learning, Information Fusion, and Applications |url=https://ieeexplore.ieee.org/document/9068414 |journal=IEEE Journal of Selected Topics in Signal Processing |volume=14 |issue=3 |pages=478–493 |doi=10.1109/JSTSP.2020.2987728 |issn=1932-4553|arxiv=1911.03977 |bibcode=2020ISTSP..14..478Z }}</ref>

== Approaches and methods ==

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Graph-based approaches for multimodal representation learning leverage graph structure to model relationships between entities across different modalities. These methods typically represent each modality as a graph and then learn embedding that preserve cross-modal similarities, enabling more effective joint representation of heterogeneous data.<ref>{{Cite journal |last1=Ektefaie |first1=Yasha |last2=Dasoulas |first2=George |last3=Noori |first3=Ayush |last4=Farhat |first4=Maha |last5=Zitnik |first5=Marinka |date=2023-04-03 |title=Multimodal learning with graphs |journal=Nature Machine Intelligence |language=en |volume=5 |issue=4 |pages=340–350 |doi=10.1038/s42256-023-00624-6 |issn=2522-5839 |pmc=10704992 |pmid=38076673}}</ref>

One such method is '''cross-modal graph neural networks''' (CMGNNs) that extend traditional [[graph neural network]]s (GNNs) to handle data from multiple modalities by constructing graphs that capture both intra-modal and inter-modal relationships. These networks model interactions across modalities by representing them as [[Vertex (graph theory)|nodes]] and their relationships as edges.<ref>{{Cite book |last1=Liu |first1=Shubao |last2=Xie |first2=Yuan |last3=Yuan |first3=Wang |last4=Ma |first4=Lizhuang |chapter=Cross-Modality Graph Neural Network for Few-Shot Learning |date=2021-07-05 |title=2021 IEEE International Conference on Multimedia and Expo (ICME) |chapter-url=https://ieeexplore.ieee.org/document/9428405 |publisher=IEEE |pages=1–6 |doi=10.1109/ICME51207.2021.9428405 |isbn=978-1-6654-3864-3}}</ref>

Other graph-based methods include [[Graphical model|'''Probabilistic Graphical Models''']] (PGMs) such as [[deep belief network]]s (DBN) and deep [[Boltzmann machine]]s (DBM). These models can learn a joint representation across modalities, for instance, a multimodal DBN achieves this by adding a shared restricted Boltzmann Machine (RBM) hidden layer on top of modality-specific DBNs.<ref name=":0" /> Additionally, the structure of data in some domains like [[Human–computer interaction|Human-Computer Interaction]] (HCI), such as the view hierarchy of app screens, can potentially be modeled using graph-like structures. The field of graph representation learning is also relevant, with ongoing progress in developing evaluation benchmarks.<ref>{{Cite journal |last1=Chen |first1=Hongruixuan |last2=Yokoya |first2=Naoto |last3=Wu |first3=Chen |last4=Du |first4=Bo |date=2022 |title=Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning |url=https://ieeexplore.ieee.org/document/9984688 |journal=IEEE Transactions on Geoscience and Remote Sensing |volume=60 |pages=1–18 |doi=10.1109/TGRS.2022.3229027 |issn=0196-2892|arxiv=2210.00941 |bibcode=2022ITGRS..6029027C }}</ref>

=== Diffusion maps ===