Comparer des méthodes

	Classification multimodale basée sur BERT	CLIP
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2019	2021
Auteur d'origine	Kiela, D. et al.; Lu, J. et al.	Radford, A.; Kim, J. W.; et al. (OpenAI)
Type	Multimodal transformer classifier	Contrastive vision-language pretraining model
Source fondatrice	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 8748–8763.
Alias	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier	CLIP, Contrastive Language-Image Pre-training, zero-shot image classifier, visual-language model
Apparentées	2	2
Résumé	Multimodal BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling.	CLIP (Contrastive Language-Image Pretraining) is a vision-language model introduced by Radford et al. at OpenAI in 2021 that jointly learns aligned image and text representations by training on 400 million internet-sourced image-text pairs using a contrastive objective, enabling zero-shot transfer to image classification tasks without any task-specific fine-tuning.