Salīdzināt metodes

	Daudzmodālu Transformers	BERT klasifikācija
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2021	2019
Autors	Lu et al. (ViLBERT); Radford et al. (CLIP)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tips	Cross-modal attention-based deep learning model	Pre-trained language model with fine-tuning
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	4
Kopsavilkums	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.