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	Odpowiadanie na pytania multimodalne	Streszczanie multimodalnych tekstów
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2015	2018
Twórca	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Zhu et al. (pioneering MSMO framework)
Typ	Supervised multimodal learning	Generative / extractive NLP with visual input
Źródło pierwotne	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.	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.
Inne nazwy	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
Pokrewne	5	5
Podsumowanie	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.