Salīdzināt metodes

	Daudzmodālu jautājumu atbildēšana	Daudzmodālu Transformers
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2015	2019–2021
Autors	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Lu et al. (ViLBERT); Radford et al. (CLIP)
Tips	Supervised multimodal learning	Cross-modal attention-based deep learning model
Pirmavots	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.
Citi nosaukumi	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Saistītās	5	5
Kopsavilkums	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.