So sánh phương pháp

	Mô hình hóa chủ đề đa phương thức	Transformer Đa phương thức
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2003–present	2019–2021
Người khởi xướng	Blei, D. M. & Jordan, M. I. (foundational corr-LDA); extended by many authors	Lu et al. (ViLBERT); Radford et al. (CLIP)
Loại	Generative probabilistic topic model	Cross-modal attention-based deep learning model
Công trình gốc	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.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Tên gọi khác	Multimodal LDA, multi-modal topic model, cross-modal topic modeling, MM-TM	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Liên quan	6	5
Tóm tắt	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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.