Usporedite metode

	Multimodalna konvolucijska neuronska mreža	Multimodalni Transformer
Područje	Duboko učenje	Duboko učenje
Obitelj	Machine learning	Machine learning
Godina nastanka	2011	2019–2021
Tvorac	Ngiam, J. et al. / multiple groups	Lu et al. (ViLBERT); Radford et al. (CLIP)
Vrsta	Multimodal deep learning model	Cross-modal attention-based deep learning model
Temeljni izvor	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.	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.
Drugi nazivi	MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Srodne	5	5
Sažetak	A Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval.	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.