Confronta i metodi

	Risposta a domande multimodali	Transformer Multimodale
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2015	2019–2021
Ideatore	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tipo	Supervised multimodal learning	Cross-modal attention-based deep learning model
Fonte seminale	Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). VQA: Visual Question Answering. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2425–2433.	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.
Alias	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Correlati	5	5
Sintesi	Multimodal question answering (Multimodal QA) is a class of deep-learning methods that answer natural-language questions by jointly reasoning over information from multiple modalities — most commonly text and images, but also video, audio, and structured tables. Introduced prominently through the VQA benchmark in 2015, it has since expanded into a broad research area powering document understanding, medical diagnosis assistance, and embodied AI.	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.