Machine learningDeep learning / NLP / CV
多模态Word2Vec
多模态Word2Vec通过将词汇表征与感知信号（通常是图像特征）以及分布文本统计数据相结合，扩展了经典的Word2Vec框架。其结果是词向量既能捕捉语言共现模式，又能捕捉视觉意义，从而实现更丰富的语义相似性判断，并在纯文本嵌入不足的概念层面任务上表现更佳。
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来源
- Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI: 10.1613/jair.4135 ↗
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS), 26. link ↗
如何引用本页
ScholarGate. (2026, June 3). Multimodal Word2Vec (Cross-Modal Distributional Semantics). ScholarGate. https://scholargate.app/zh/deep-learning/multimodal-word2vec
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