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| 다중 양식 확산 모델× | 멀티모달 BERT 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020–2022 | 2019 |
| 창시자≠ | Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion) | Kiela, D. et al.; Lu, J. et al. |
| 유형≠ | Generative model (denoising diffusion) | Multimodal transformer classifier |
| 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 ↗ | Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950. link ↗ |
| 별칭 | multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion | MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier |
| 관련≠ | 6 | 2 |
| 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 BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling. |
| ScholarGate데이터셋 ↗ |
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