So sánh phương pháp

	Phân loại dựa trên RoBERTa Đa phương thức	Transformer Đa phương thức
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019–2020	2019–2021
Người khởi xướng	Liu et al. (RoBERTa); multimodal extension by community	Lu et al. (ViLBERT); Radford et al. (CLIP)
Loại	Multimodal text + auxiliary feature classification	Cross-modal attention-based deep learning model
Công trình gốc	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.
Tên gọi khác	Multimodal RoBERTa, RoBERTa multimodal classifier, cross-modal RoBERTa classification, MM-RoBERTa	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Liên quan	6	5
Tóm tắt	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.