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| 다중 모달 트랜스포머× | 이미지 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019–2021 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| 창시자≠ | Lu et al. (ViLBERT); Radford et al. (CLIP) | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| 유형≠ | Cross-modal attention-based deep learning model | Supervised classification task |
| 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 ↗ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ |
| 별칭 | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer | visual classification, image recognition, CNN-based classification, visual categorization |
| 관련 | 5 | 5 |
| 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. | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. |
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