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KAT: A Knowledge Augmented Transformer for Vision-and-Language
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Liangke Gui, Borui Wang, Qiuyuan Huang, Alexander Hauptmann, Yonatan Bisk, Jianfeng Gao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
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“Recent research suggests that language models serve a function similar to that of traditional knowledge bases in retrieving information [248, 249, 287, 289].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [289]
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Wang, C., Zhang, L., & Zhou, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113711 · Cited by 2
AFSPrompt: An Axiomatic Fuzzy Set Prompt Pipeline for Knowledge-Based VQA
Wang, Z., Lang, Q., Liu, X., & Zhang, H. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3573267 · Cited by 1
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization
Salemi, A., & Zamani, H. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744584 · Cited by 16
QC 2 -VQG: Question context complement for visual question generation
Zhang, Y., Liu, X., Lu, Z., Guo, W., Liu, X., & Yan, R. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114306 · Cited by 1
Min, Y., Sun, Y., Zhu, Y., Zhu, J., & Zhang, B. (2026) · Machine Intelligence Research · doi:10.1007/s11633-025-1591-z · Cited by 1
Effective Retrieval Augmentation for Knowledge-Based Vision Question Answering
Deng, J. (2024) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc64747.2024.10780559 · Cited by 1
TAG: Triple Alignment With Rationale Generation for Knowledge-Based Visual Question Answering
Cai, S., Lin, X., Xu, W., Wu, J., Jin, T., Zhao, Z., Wu, F. Q., & Yu, J. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3600012 · Cited by 1
Dynamic Alternative Attention for Visual Question Answering
Liu, X., Guo, W., Zhang, Y., & Zhang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20309-1_33
Conclusions and Research Trends
Gao, J., Xiong, C., Bennett, P. N., & Craswell, N. (2023) · The information retrieval series · doi:10.1007/978-3-031-23080-6_9
Self-citationLiang, M., Liu, Z., & Larson, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53302-0_16
Outside-Knowledge Visual Question Answering for Visual Impaired People
Sun, Y., Si, Q., Lin, Z., Wang, W., & Mei, T. (2023) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai59581.2023.00014
A Comprehensive Review and Open Challenges on Visual Question Answering Models
Shaik, F. A., Koshti, D., Gupta, A., Kalla, M., & Sharma, A. (2023) · Aibi revista de investigación administración e ingeniería · doi:10.15649/2346030x.3370
Semi-Supervised Implicit Augmentation for Data-Scarce VQA
Dodla, B., Hegde, K., & Rajagopalan, A. N. (2024) · doi:10.3390/cmsf2024009003
A Reinforced Passage Interactive Retrieval Framework Incorporating Implicit Knowledge for KB-VQA
Zheng, H., Sun, T., Zhang, Z., Liu, H., Liu, G., Yang, Z., & Wang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5615-5_20
Chai, N., Zou, D., Liu, J., Wang, H., Yang, Y., & Song, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9437-9_31
Metadata
- Authors
- Gui, L., Wang, B., Huang, Q., Hauptmann, A., Bisk, Y., & Gao, J.
- Affiliations
- Yale University (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 956-968
- OpenAlex
- W4226321975
- Cited by
- 130
- References
- 38
- Citations by year
- 2026: 17 · 2025: 35 · 2024: 34 · 2023: 36 · 2022: 8
- FWCI
- 10.25
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- DeepMind
- Defense Advanced Research Projects Agency (FA8750-19-2-1004, FA8750)
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- knowledge improvement, vision-language models, transformer models, knowledge graphs, multimodal representation learning
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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