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| 다중 양식 질의응답× | 멀티모달 BERT 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015 | 2019 |
| 창시자≠ | Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech) | Kiela, D. et al.; Lu, J. et al. |
| 유형≠ | Supervised multimodal learning | Multimodal transformer classifier |
| 원전≠ | 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. DOI ↗ | Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950. link ↗ |
| 별칭 | Multimodal QA, Cross-modal question answering, Visual question answering, VQA | MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier |
| 관련≠ | 5 | 2 |
| 요약≠ | 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 BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling. |
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