Linganisha mbinu

	Kielelezo cha Mada cha Multimodal LDA	Transformeri wa Multimodal
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2003	2019–2021
Mwanzilishi	Blei, D. M. & Jordan, M. I.	Lu et al. (ViLBERT); Radford et al. (CLIP)
Aina	Probabilistic generative topic model (multimodal)	Cross-modal attention-based deep learning model
Chanzo asilia	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.
Majina mbadala	Multimodal LDA, mm-LDA, multimodal topic model, cross-modal LDA	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Zinazohusiana	6	5
Muhtasari	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.	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.