Methoden vergleichen

	Multimodale Fragebeantwortung	Multimodale Textzusammenfassung
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2015	2018
Urheber	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
Wegweisende Quelle	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.
Aliasnamen	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
Verwandt	5	5
Zusammenfassung	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.