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| Multimodal Named Entity Recognition× | Multimodal Transformer× | |
|---|---|---|
| Fagområde | Dyb læring | Dyb læring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2018 | 2019–2021 |
| Ophavsperson≠ | Moon, S.; Lu, D. et al. | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| Type≠ | Sequence labeling with multimodal fusion | Cross-modal attention-based deep learning model |
| Oprindelig kilde≠ | 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. link ↗ | 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. link ↗ |
| Aliasser | Multimodal NER, MNER, Visual NER, Cross-modal Named Entity Recognition | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | 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. |
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