방법 비교

	다중 모달 RoBERTa 기반 분류	BERT 기반 분류
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2019–2020	2019
창시자	Liu et al. (RoBERTa); multimodal extension by community	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
유형	Multimodal text + auxiliary feature classification	Pre-trained language model with fine-tuning
원전	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.	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.
별칭	Multimodal RoBERTa, RoBERTa multimodal classifier, cross-modal RoBERTa classification, MM-RoBERTa	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
관련	6	4
요약	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.	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.