قارن الطرق

	المحولات متعددة الوسائط (Multimodal Transformers)	تصنيف قائم على BERT
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)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
النوع	Cross-modal attention-based deep learning model	Pre-trained language model with fine-tuning
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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
الأسماء البديلة	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
ذات صلة	5	4
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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.