Measuring Similarity of Dual-Modal Academic Data Based on Multi-Fusion Representation Learning Article Swipe
Li Zhang
,
Qiang Gao
,
Ming Liu
,
Zepeng Gu
,
Bo Lang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3427731
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3427731
Nowadays, academic materials such as articles, patents, lecture notes, and observation records often use both texts and images (i.e., dual-modal data) to illustrate scientific issues. Measuring the similarity of such dual-modal academic data largely depends on dual-modal features, which is far from satisfying in practice. To learn dual-modal feature representation, most current approaches mine interactions between texts and images on top of their fusion networks. This work proposes a multi-fusion deep learning framework that learns semantically richer dual-modal representations. The framework designs multiple fusion points in the feature space of various levels, and gradually integrates the fusion information from the low-level to the high-level. In addition, we develop a multi-channel decoding network with alternate fine-tuning strategies to mine modal-specific features and cross-modal correlations thoroughly. To our knowledge, this is the first work to bring forward deep learning functions for dual-modal academic data. It reduces the semantic and statistical attribute differences between two modalities, thereby learning robust representations. A large number of experiments conducted on real-world data sets show that our method has significant performance compared with state-of-the-art approaches.
Related Topics
Concepts
Modal
Computer science
Dual (grammatical number)
Similarity (geometry)
Representation (politics)
Artificial intelligence
Modalities
Feature (linguistics)
Feature learning
Sensor fusion
Machine learning
External Data Representation
Data mining
Pattern recognition (psychology)
Image (mathematics)
Political science
Law
Art
Philosophy
Linguistics
Social science
Polymer chemistry
Politics
Sociology
Literature
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3427731
- OA Status
- gold
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400645628
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400645628Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3427731Digital Object Identifier
- Title
-
Measuring Similarity of Dual-Modal Academic Data Based on Multi-Fusion Representation LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Li Zhang, Qiang Gao, Ming Liu, Zepeng Gu, Bo LangList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3427731Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2024.3427731Direct OA link when available
- Concepts
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Modal, Computer science, Dual (grammatical number), Similarity (geometry), Representation (politics), Artificial intelligence, Modalities, Feature (linguistics), Feature learning, Sensor fusion, Machine learning, External Data Representation, Data mining, Pattern recognition (psychology), Image (mathematics), Political science, Law, Art, Philosophy, Linguistics, Social science, Polymer chemistry, Politics, Sociology, Literature, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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57Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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