Comparer des méthodes

	Reconnaissance multimodale d'entités nommées	Plongements de phrases multimodaux
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2018	2013–2021
Auteur d'origine	Moon, S.; Lu, D. et al.	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
Type	Sequence labeling with multimodal fusion	Representation learning model
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.	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.
Alias	Multimodal NER, MNER, Visual NER, Cross-modal Named Entity Recognition	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
Apparentées	6	1
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 sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning.