方法对比
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| 多模态Word2Vec× | 多模态Transformer× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 | 2019–2021 |
| 提出者≠ | Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.) | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 类型≠ | Multimodal word embedding model | Cross-modal attention-based deep learning model |
| 开创性文献≠ | Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI ↗ | 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 ↗ |
| 别名 | multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 相关 | 5 | 5 |
| 摘要≠ | Multimodal Word2Vec extends the classic Word2Vec framework by grounding word representations in perceptual signals — typically image features — alongside distributional text statistics. The result is word vectors that capture both linguistic co-occurrence patterns and visual meaning, enabling richer semantic similarity judgements and better performance on concept-level tasks where purely text-based embeddings fall short. | 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. |
| ScholarGate数据集 ↗ |
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