Comparer des méthodes

	Classification multimodale basée sur RoBERTa	Plongements de phrases multimodaux
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2019–2020	2013–2021
Auteur d'origine	Liu et al. (RoBERTa); multimodal extension by community	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
Type	Multimodal text + auxiliary feature classification	Representation learning model
Source fondatrice	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.	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.
Alias	Multimodal RoBERTa, RoBERTa multimodal classifier, cross-modal RoBERTa classification, MM-RoBERTa	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
Apparentées	6	1
Résumé	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.	Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning.