方法对比
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| 多模态Doc2Vec× | 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≠ | 2014–2017 | 2013–2021 |
| 提出者≠ | Le, Q. V. & Mikolov, T. (Doc2Vec core); multimodal extensions by various authors post-2014 | Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021) |
| 类型≠ | Multimodal document embedding | Representation learning model |
| 开创性文献≠ | Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2), 1188–1196. 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 Paragraph Vector, Cross-modal Doc2Vec, Multi-source PV-DM, Multimodal Document Embedding | multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings |
| 相关≠ | 6 | 1 |
| 摘要≠ | Multimodal Doc2Vec extends the Doc2Vec paragraph-vector framework to incorporate information from more than one modality — typically text alongside images, audio, or structured metadata — producing a shared document-level embedding that captures semantics from multiple sources simultaneously. It is used for cross-modal retrieval, multi-source classification, and document representation where text alone is insufficient. | 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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