विधियों की तुलना करें

	बहुविध प्रश्न उत्तरण (Multimodal Question Answering)	मल्टीमॉडल टेक्स्ट समराइज़ेशन (Multimodal Text Summarization)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2015	2018
प्रवर्तक	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Zhu et al. (pioneering MSMO framework)
प्रकार	Supervised multimodal learning	Generative / extractive NLP with visual input
मौलिक स्रोत	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.
उपनाम	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	MMS, multimodal summarization, cross-modal summarization, vision-language summarization
संबंधित	5	5
सारांश	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.