Machine learningDeep learning / NLP / CV
多模态主题建模
多模态主题建模通过学习联合概率表示来发现跨越多种数据模态（例如，共现的词语和图像）的潜在主题结构，该表示能够对齐跨模态的主题。它将 LDA 等经典仅文本方法扩展到每个文档或观测由异构数据类型组成的场景。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134. DOI: 10.1145/860435.860460 ↗
- Ramage, D., Dumais, S., & Liebling, D. (2010). Characterizing microblogs with topic models. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 130–137. link ↗
如何引用本页
ScholarGate. (2026, June 3). Multimodal Topic Modeling (Joint Probabilistic Topic Discovery across Multiple Modalities). ScholarGate. https://scholargate.app/zh/deep-learning/multimodal-topic-modeling
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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