Salīdzināt metodes

	Daudzmodālu nosaukto entitāšu atpazīšana	Daudzmodālu Transformers
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018	2019–2021
Autors	Moon, S.; Lu, D. et al.	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tips	Sequence labeling with multimodal fusion	Cross-modal attention-based deep learning model
Pirmavots	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.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Citi nosaukumi	Multimodal NER, MNER, Visual NER, Cross-modal Named Entity Recognition	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Saistītās	6	5
Kopsavilkums	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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.