Methoden vergelijken

	Multimodale RoBERTa-gebaseerde Classificatie	Multimodale Transformer
Vakgebied	Deep learning	Deep learning
Familie	Machine learning	Machine learning
Jaar van ontstaan	2019–2020	2019–2021
Grondlegger	Liu et al. (RoBERTa); multimodal extension by community	Lu et al. (ViLBERT); Radford et al. (CLIP)
Type	Multimodal text + auxiliary feature classification	Cross-modal attention-based deep learning model
Oorspronkelijke bron	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.
Aliassen	Multimodal RoBERTa, RoBERTa multimodal classifier, cross-modal RoBERTa classification, MM-RoBERTa	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Verwant	6	5
Samenvatting	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.