Comparer des méthodes

	Modélisation thématique multimodale	Classification multimodale basée sur BERT
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2003–present	2019
Auteur d'origine	Blei, D. M. & Jordan, M. I. (foundational corr-LDA); extended by many authors	Kiela, D. et al.; Lu, J. et al.
Type	Generative probabilistic topic model	Multimodal transformer classifier
Source fondatrice	Blei, D. M., & Jordan, M. I. (2003). Modeling annotated data. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 127–134.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Alias	Multimodal LDA, multi-modal topic model, cross-modal topic modeling, MM-TM	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Apparentées	6	2
Résumé	Multimodal topic modeling discovers latent thematic structure shared across multiple data modalities — for example, co-occurring words and images — by learning a joint probabilistic representation that aligns topics across modalities. It extends classical text-only approaches such as LDA to settings where each document or observation consists of heterogeneous data types.	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.