Salīdzināt metodes

	Multimodāls difūzijas modelis	Daudzmodālu Transformers
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020–2022	2019–2021
Autors	Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tips	Generative model (denoising diffusion)	Cross-modal attention-based deep learning model
Pirmavots	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.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Citi nosaukumi	multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Saistītās	6	5
Kopsavilkums	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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.