Сравнение методов

	Мультимодальный ответ на вопросы	Мультимодальное реферирование текстов
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2015	2018
Автор метода	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Zhu et al. (pioneering MSMO framework)
Тип	Supervised multimodal learning	Generative / extractive NLP with visual input
Основополагающий источник	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.
Другие названия	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
Связанные	5	5
Сводка	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.