قارن الطرق

	المحولات متعددة الوسائط (Multimodal Transformers)	التصنيف المعتمد على نموذج BERT متعدد الوسائط
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2019–2021	2019
صاحب الطريقة	Lu et al. (ViLBERT); Radford et al. (CLIP)	Kiela, D. et al.; Lu, J. et al.
النوع	Cross-modal attention-based deep learning model	Multimodal transformer classifier
المصدر التأسيسي	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.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
الأسماء البديلة	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
ذات صلة	5	2
الملخص	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.	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.