Compara mètodes

	Preguntes i Respostes Multimodal	Multimodal Sentence Embeddings
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2015	2013–2021
Autor original	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
Tipus	Supervised multimodal learning	Representation learning model
Font seminal	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.	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.
Àlies	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
Relacionats	5	1
Resum	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.	Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning.