방법 비교

	Multimodal LDA Topic Model	멀티모달 BERT 기반 분류
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2003	2019
창시자	Blei, D. M. & Jordan, M. I.	Kiela, D. et al.; Lu, J. et al.
유형	Probabilistic generative topic model (multimodal)	Multimodal transformer classifier
원전	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.
별칭	Multimodal LDA, mm-LDA, multimodal topic model, cross-modal LDA	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
관련	6	2
요약	Multimodal LDA extends Latent Dirichlet Allocation to jointly model multiple data modalities — most often text and images — within a single probabilistic topic framework. Each document or data instance is represented as a mixture of latent topics shared across modalities, enabling the model to discover coherent themes that align visual and linguistic content simultaneously.	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.