Compara mètodes

	Preguntes i Respostes Multimodal	Resum de text multimodal
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2015	2018
Autor original	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Zhu et al. (pioneering MSMO framework)
Tipus	Supervised multimodal learning	Generative / extractive NLP with visual input
Font seminal	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.
Àlies	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
Relacionats	5	5
Resum	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.