方法对比
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| 多模态 RoBERTa 分类× | 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≠ | 2019–2020 | 2013–2021 |
| 提出者≠ | Liu et al. (RoBERTa); multimodal extension by community | Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021) |
| 类型≠ | Multimodal text + auxiliary feature classification | Representation learning model |
| 开创性文献≠ | Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692. 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 RoBERTa, RoBERTa multimodal classifier, cross-modal RoBERTa classification, MM-RoBERTa | multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings |
| 相关≠ | 6 | 1 |
| 摘要≠ | Multimodal RoBERTa-based Classification combines the RoBERTa transformer encoder — a robustly optimised variant of BERT — with auxiliary modalities such as images, structured metadata, or tabular features. The fused representation is passed to a classification head, allowing the model to leverage both rich language understanding and non-textual signals simultaneously. | 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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