विधियों की तुलना करें

	बहुविध प्रश्न उत्तरण (Multimodal Question Answering)	बहुविध वाक्य एम्बेडिंग (Multimodal Sentence Embeddings)
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2015	2013–2021
प्रवर्तक	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
प्रकार	Supervised multimodal learning	Representation learning model
मौलिक स्रोत	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.
उपनाम	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
संबंधित	5	1
सारांश	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.