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| 다중 양식 이미지 분류× | 다중 모달 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2011–2021 | 2019–2021 |
| 창시자≠ | Ngiam et al.; Radford et al. (CLIP) | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 유형≠ | Multimodal supervised classification | Cross-modal attention-based deep learning model |
| 원전≠ | Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139, 8748–8763. link ↗ | 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 ↗ |
| 별칭 | multimodal visual classification, image-text classification, vision-language classification, cross-modal image classification | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 관련≠ | 6 | 5 |
| 요약≠ | Multimodal image classification extends standard visual classification by incorporating additional modalities — such as text captions, audio, or structured metadata — alongside image features. Separate encoders process each modality, their representations are fused, and a joint classifier assigns the target label. Models such as CLIP demonstrate that image–text alignment enables zero-shot and few-shot image classification at scale. | 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. |
| ScholarGate데이터셋 ↗ |
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