方法对比
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| 多模态问题解答× | [需翻译标题：BERT-based Classification...]× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015 | 2019 |
| 提出者≠ | Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech) | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| 类型≠ | Supervised multimodal learning | Pre-trained language model with fine-tuning |
| 开创性文献≠ | 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 ↗ | 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. DOI ↗ |
| 别名 | Multimodal QA, Cross-modal question answering, Visual question answering, VQA | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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