Machine learningDeep learning / NLP / CV
多模态扩散模型
多模态扩散模型是对去噪扩散概率模型 (denoising diffusion probabilistic models) 的一种扩展，它通过同时对来自多种模态（如文本、图像、音频或视频）的信号进行条件化，来生成或理解内容。它学习逆转一个受跨模态上下文引导的噪声过程，从而实现跨模态的高保真合成与转换。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10684–10695. DOI: 10.1109/CVPR52688.2022.01042 ↗
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗
如何引用本页
ScholarGate. (2026, June 3). Multimodal Diffusion Model (Cross-Modal Conditional Denoising Diffusion). ScholarGate. https://scholargate.app/zh/deep-learning/multimodal-diffusion-model
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.
- 微调扩散模型深度学习↔ compare
- 多模态BERT分类深度学习↔ compare
- 多模态生成对抗网络深度学习↔ compare
- 多模态Transformer深度学习↔ compare
- 多模态变分自编码器深度学习↔ compare
- 多模态视觉变换器深度学习↔ compare