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| 다중 양식 질의응답× | 다중 양식 문장 임베딩× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015 | 2013–2021 |
| 창시자≠ | Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech) | Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021) |
| 유형≠ | Supervised multimodal learning | Representation learning model |
| 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 ↗ | 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. link ↗ |
| 별칭 | Multimodal QA, Cross-modal question answering, Visual question answering, VQA | multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings |
| 관련≠ | 5 | 1 |
| 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 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. |
| ScholarGate데이터셋 ↗ |
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