Machine learningDeep learning / NLP / CV
多模态LSTM
多模态LSTM（Multimodal LSTM）是对标准长短期记忆网络（Long Short-Term Memory network）的扩展，旨在在一个统一的循环架构中联合处理来自多种输入模态（如文本、音频和视频）的序列数据。通过在LSTM单元之前或之内融合不同来源的表征，它可以捕捉跨越和贯穿模态的时间依赖性，使其成为情感分析、视频字幕生成和情感计算等任务的基础性方法。
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来源
- Rajagopalan, S., Tran, L., Rozgic, V., Narayanan, S., Kumar, A., & Ramakrishna, S. (2016). Extending Long Short-Term Memory for Multi-View Structured Learning. In Proceedings of ECCV 2016. Springer. link ↗
- Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735 ↗
如何引用本页
ScholarGate. (2026, June 3). Multimodal Long Short-Term Memory Network. ScholarGate. https://scholargate.app/zh/deep-learning/multimodal-lstm
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