Methoden vergleichen

	Multimodale Textzusammenfassung	Multimodale Fragebeantwortung
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2018	2015
Urheber	Zhu et al. (pioneering MSMO framework)	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)
Typ	Generative / extractive NLP with visual input	Supervised multimodal learning
Wegweisende Quelle	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.	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.
Aliasnamen	MMS, multimodal summarization, cross-modal summarization, vision-language summarization	Multimodal QA, Cross-modal question answering, Visual question answering, VQA
Verwandt	5	5
Zusammenfassung	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.	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.