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| 다중 양식 확산 모델× | 멀티모달 비전 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020–2022 | 2021 |
| 창시자≠ | Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion) | Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT) |
| 유형≠ | Generative model (denoising diffusion) | Multimodal transformer model |
| 원전≠ | 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 ↗ | Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR). link ↗ |
| 별칭 | multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion | Multimodal ViT, vision-language transformer, cross-modal vision transformer, multi-modal ViT |
| 관련≠ | 6 | 5 |
| 요약≠ | A multimodal diffusion model extends denoising diffusion probabilistic models to generate or understand content by conditioning on signals from multiple modalities — such as text, image, audio, or video — simultaneously. It learns to reverse a noise process guided by cross-modal context, enabling high-fidelity synthesis and translation across modalities. | Multimodal Vision Transformer (Multimodal ViT) extends the Vision Transformer architecture to jointly process and align representations from multiple modalities — typically images and text — using self-attention and cross-attention mechanisms. By learning shared or aligned embedding spaces across modalities, it enables tasks such as visual question answering, image-text retrieval, visual grounding, and image captioning. |
| ScholarGate데이터셋 ↗ |
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