Comparer des méthodes

	Reconnaissance multimodale d'entités nommées	Réponse aux questions multimodales
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2018	2015
Auteur d'origine	Moon, S.; Lu, D. et al.	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)
Type	Sequence labeling with multimodal fusion	Supervised multimodal learning
Source fondatrice	Moon, S., Neves, L., & Carvalho, V. (2018). Multimodal Named Entity Recognition for Short Social Media Posts. Proceedings of NAACL-HLT 2018, pp. 852–860. Association for Computational Linguistics.	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.
Alias	Multimodal NER, MNER, Visual NER, Cross-modal Named Entity Recognition	Multimodal QA, Cross-modal question answering, Visual question answering, VQA
Apparentées	6	5
Résumé	Multimodal Named Entity Recognition (MNER) extends classical NER by fusing textual sequences with complementary modalities — most commonly images — to improve the identification and classification of named entities such as persons, organizations, and locations in settings where visual context disambiguates ambiguous or sparse text.	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.