পদ্ধতির তুলনা করুন

	মাল্টিমোডাল প্রশ্ন উত্তর (Multimodal Question Answering)	BERT-ভিত্তিক শ্রেণিবিভাগ
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015	2019
প্রবর্তক	Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
ধরন	Supervised multimodal learning	Pre-trained language model with fine-tuning
মৌলিক উৎস	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
অপর নাম	Multimodal QA, Cross-modal question answering, Visual question answering, VQA	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
সম্পর্কিত	5	4
সারসংক্ষেপ	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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.