Vision to Language: Methods, Metrics and Datasets
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Most previous multimodal benchmarks have focused on a single research area such as within affective computing, human multimodal language, language and vision-based question answering, text classification with external multimodal information, and multimodal learning for education.
“Most previous multimodal benchmarks have focused on a single research area such as within affective computing [56], human multimodal language [177], language and vision-based question answering [50, 138], text classification with external multimodal information [60], and multimodal learning for education [65].”
From MultiBench: Multiscale Benchmarks for Multimodal Representation Learning (2021) · cites this paper as [138]
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Multimodal Pivots for Image Caption Translation
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Fluency-Guided Cross-Lingual Image Captioning
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Phrase-based Image CaptioningPreprint
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Visual StorytellingPreprint
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Generating Image Descriptions Using Dependency Relational Patterns
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A Survey of Commonsense Knowledge Acquisition
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Deep Learning for Scene Understanding
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1 Million Captioned Dutch Newspaper Images
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The Lexical Gap: An Improved Measure of Automated Image Description Quality
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Flagged first, then by citations
43 of the 202 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
From Show to Tell: A Survey on Deep Learning-Based Image Captioning
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MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
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“…multimodal benchmarks have focused on a single research area such as within affective computing [56], human multimodal language [177], language and vision-based question answering [50, 138], text classification with external multimodal information [60], and multimodal learning for education [65].”
BackgroundJamil, A., Rehman, S. U., Mahmood, K., Villar, M. G., Prola, T. A., Díez, I. D. L. T., Samad, M. A., & Ashraf, I. (2024) · IEEE Access · doi:10.1109/access.2024.3365528 · Cited by 23 · see citing works
RetractedEvaluation metrics for video captioning: A survey
Inácio, A. D. S., & Lopes, H. S. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100488 · Cited by 8 · see citing works
Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning
Sharif, N., Jalwana, M. A. A. K., Bennamoun, M., Liu, W., & Shah, S. A. A. (2020) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz51579.2020.9290719 · Cited by 4 · see citing works
Self-citationGondim, J. M., Claro, D. B., & Souza, M. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5792 · Cited by 2 · see citing works
Cross-multi-modal seamless training for image captioning
Hossain, M. S., Aktar, S., Babar, A. H., Khan, M. F., Huang, X., Gu, N., & Huang, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130653 · Cited by 2 · see citing works
Reflecting on machine-generated image descriptions of personal photos
Jung, J. Y., & Kim, J. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3677045.3685494 · Cited by 1 · see citing works
Deep Learning-Based Automatic Creation of Image and Video Captions: A Review
K, H. R. R., B, V. K. S., -, K. H. M., Jayashree, R., Mekala, N., & Tharunlal, P. M. S. (2024) · International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM) · doi:10.1109/iipem62726.2024.10925745
- Authors
- Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M., & Liu, W.
- Affiliations
- The University of Western Australia (AU)
- Murdoch University (AU)
- Corresponding author
- Nadeem, Uzair
- Venue
- Learning and Analytics in Intelligent Systems
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 9-62
- ISSN
- 2662-3447 (ISSN-L)
- 2662-3455
- OpenAlex
- W3045097470
- Cited by
- 9
- References
- 202
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 1
- FWCI
- 1.96
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Learning and analytics in intelligent systems (publishedVersion)
- Edith Cowan University Research Online (Edith Cowan University) (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- 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, vision-language models, image-text generation, multimodal fusion, visual description generation
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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