方法对比
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| 多模态命名实体识别× | 多模态句子嵌入× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018 | 2013–2021 |
| 提出者≠ | Moon, S.; Lu, D. et al. | Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021) |
| 类型≠ | Sequence labeling with multimodal fusion | Representation learning model |
| 开创性文献≠ | 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 ↗ | 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. link ↗ |
| 别名 | Multimodal NER, MNER, Visual NER, Cross-modal Named Entity Recognition | multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings |
| 相关≠ | 6 | 1 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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