قارن الطرق

	نموذج الانتشار متعدد الوسائط	المحولات متعددة الوسائط (Multimodal Transformers)
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2020–2022	2019–2021
صاحب الطريقة	Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion)	Lu et al. (ViLBERT); Radford et al. (CLIP)
النوع	Generative model (denoising diffusion)	Cross-modal attention-based deep learning model
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	6	5
الملخص	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.