Salīdzināt metodes

	Multimodāla RoBERTa klasifikācija	Daudzmodālu Transformers
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2020	2019–2021
Autors	Liu et al. (RoBERTa); multimodal extension by community	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tips	Multimodal text + auxiliary feature classification	Cross-modal attention-based deep learning model
Pirmavots	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.	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 RoBERTa, RoBERTa multimodal classifier, cross-modal RoBERTa classification, MM-RoBERTa	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Saistītās	6	5
Kopsavilkums	Multimodal RoBERTa-based Classification combines the RoBERTa transformer encoder — a robustly optimised variant of BERT — with auxiliary modalities such as images, structured metadata, or tabular features. The fused representation is passed to a classification head, allowing the model to leverage both rich language understanding and non-textual signals simultaneously.	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.