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| 다중 모달 트랜스포머× | 멀티모달 BERT 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019–2021 | 2019 |
| 창시자≠ | Lu et al. (ViLBERT); Radford et al. (CLIP) | Kiela, D. et al.; Lu, J. et al. |
| 유형≠ | Cross-modal attention-based deep learning model | Multimodal transformer classifier |
| 원전≠ | Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗ | 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 attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer | MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier |
| 관련≠ | 5 | 2 |
| 요약≠ | A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis. | 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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