Process / pipeline
多模态自然语言处理 — 视觉语言理解
多模态自然语言处理（Multimodal NLP）是一类自然语言处理流水线，它将文本与一种或多种额外的数据模态——最常见的是图像，但也包括音频和视频——相结合，以执行理解和生成任务，例如视觉问答、图像字幕生成和多模态情感识别。该领域随着 CLIP (Radford et al., 2021) 的出现而形成现代形态，并在此后通过 BLIP-2 (Li et al., 2023) 等架构取得了进展，这些架构连接了冻结的图像编码器和大型语言模型。
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来源
- Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), 8748–8763. link ↗
- Li, J., Li, D., Savarese, S., & Hoi, S. (2023). BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. Proceedings of the 40th International Conference on Machine Learning (ICML), 19730–19742. link ↗
如何引用本页
ScholarGate. (2026, June 1). Multimodal Natural Language Processing. ScholarGate. https://scholargate.app/zh/text-mining/multimodal-nlp
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