A multimodal fusion approach for image captioning
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Recurrent neural networks have proved useful in multimodal problems requiring modeling long- and short-range dependencies across the input sequence, such as semantic segmentation and image captioning.
“In addition to their use for unimodal tasks, RNNs have proved useful in many multimodal problems that require modeling long- and short-range dependencies across the input sequence, such as semantic segmentation [219] and image captioning [220].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [220]
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Image Captioning with Complementary Visual and Textual CuesPreprint
venkatesan, B., & Sond, R. K. (2020) · doi:10.36227/techrxiv.12093564.v1 · Cited by 1 · see citing works
Image Captioning with Complementary Visual and Textual CuesPreprint
venkatesan, B., & Sond, R. K. (2020) · doi:10.36227/techrxiv.12093564
Goenka, A. (2022) · International Journal of Research in Science and Technology · doi:10.37648/ijrst.v12i03.009
SAM-MFF: An Image Captioning Generation Method Based on Multi-layer Feature Fusion
He, Z., Qi, X., Ma, S., Hou, Z., & Yang, X. (2024) · doi:10.1145/3703935.3704097
On the Use of Cross-Attentive Fusion Techniques for Audio-Visual Speaker Verification
Alam, J., & Praveen, R. G. (2025) · Machine translation · doi:10.1007/978-3-031-85747-8_9
Kurlekar, S., & Dixit, M. R. (2025) · African Journal Of Applied Research · doi:10.26437/ke5qv411
Medical Image Captioning in Radiology Reporting Using Artificial Intelligence
Abed, E. A., & Aguili, T. (2025) · International Conference on Communications and Networking (ComNet) · doi:10.1109/comnet68251.2025.11325450
Image mask-guided cross-modal network for radiology report generation
Liu, Y., Hou, X., Li, X., & Zhang, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133027
- Authors
- Zhao, D., Chang, Z., & Guo, S.
- Affiliations
- Tianjin University of Technology (CN)
- Corresponding author
- Chang, Zhi
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 329
- Pages
- 476-485
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2900323013
- Cited by
- 61
- References
- 60
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 11 · 2023: 9 · 2022: 17 · 2021: 6 · 2020: 6 · 2019: 4
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image captioning, multimodal fusion, long-range dependencies, word prediction, visual feature representation, gradient vanishing, Flickr8k, Flickr30k, MS COCO
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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