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| 다중 양식 질의응답× | 다중 양식 텍스트 요약× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015 | 2018 |
| 창시자≠ | Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech) | Zhu et al. (pioneering MSMO framework) |
| 유형≠ | Supervised multimodal learning | Generative / extractive NLP with visual input |
| 원전≠ | Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). VQA: Visual Question Answering. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2425–2433. DOI ↗ | Zhu, J., Li, H., Liu, T., Zhou, Y., Zhang, J., & Zong, C. (2018). MSMO: Multimodal Summarization with Multimodal Output. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4154–4164. link ↗ |
| 별칭 | Multimodal QA, Cross-modal question answering, Visual question answering, VQA | MMS, multimodal summarization, cross-modal summarization, vision-language summarization |
| 관련 | 5 | 5 |
| 요약≠ | Multimodal question answering (Multimodal QA) is a class of deep-learning methods that answer natural-language questions by jointly reasoning over information from multiple modalities — most commonly text and images, but also video, audio, and structured tables. Introduced prominently through the VQA benchmark in 2015, it has since expanded into a broad research area powering document understanding, medical diagnosis assistance, and embodied AI. | Multimodal text summarization generates a concise textual summary by jointly processing multiple input modalities — most commonly text and images, but also video frames or audio — using deep learning models that align visual and linguistic representations. The output is a natural-language summary that captures salient content from all available modalities. |
| ScholarGate데이터셋 ↗ |
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