The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
The multi-modal fusion in visual question answering: a review of attention mechanisms
Open accessAbstract
Visual Question Answering (VQA) is a significant cross-disciplinary issue in the fields of computer vision and natural language processing that requires a computer to output a natural language answer based on pictures and questions posed based on the pictures. This requires simultaneous processing of multimodal fusion of text features and visual features, and the key task that can ensure its success is the attention mechanism. Bringing in attention mechanisms makes it better to integrate text features and image features into a compact multi-modal representation. Therefore, it is necessary to clarify the development status of attention mechanism, understand the most advanced attention mechanism methods, and look forward to its future development direction. In this article, we first conduct a bibliometric analysis of the correlation through CiteSpace, then we find and reasonably speculate that the attention mechanism has great development potential in cross-modal retrieval. Secondly, we discuss the classification and application of existing attention mechanisms in VQA tasks, analysis their shortcomings, and summarize current improvement methods. Finally, through the continuous exploration of attention mechanisms, we believe that VQA will evolve in a smarter and more human direction.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Squeeze-and-Excitation Networks
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Research works printed but not linked: 1
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Cited by
(1996) · Institutional Research Information System University of Turin (University of Turin) · doi:10.1111/j · Cited by 5,486
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,495
“Following that, we come to the idea of responsible artificial intelligence, which is an AI that takes into account societal values as well as moral and ethical issues to improve the usability of AI models in real-world applications [27, 28].”
BackgroundVehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences
Al Mudawi, N., Qureshi, A. M., Abdelhaq, M., Alshahrani, A., Alazeb, A., Alonazi, M., & Algarni, A. (2023) · Sustainability · doi:10.3390/su151914597 · Cited by 68
Metadata
- Authors
- Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., & Zheng, W.
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2023-05-30
- Volume
- 9
- Pages
- e1400
- ISSN
- 2376-5992 (ISSN-L)
- OpenAlex
- W4381598461
- Cited by
- 477
- References
- 113
- FWCI
- 26.46
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- The Sichuan Science and Technology Program (2021YFQ0003)
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- Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., & Zheng, W. (2023). The multi-modal fusion in visual question answering: a review of attention mechanisms. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.1400
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
