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| 멀티모달 비전 트랜스포머× | Vision Transformer× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2021 | 2021 |
| 창시자≠ | Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT) | Dosovitskiy, A. et al. |
| 유형≠ | Multimodal transformer model | Transformer architecture for images (self-attention over patches) |
| 원전≠ | Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR). link ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| 별칭 | Multimodal ViT, vision-language transformer, cross-modal vision transformer, multi-modal ViT | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| 관련 | 5 | 5 |
| 요약≠ | Multimodal Vision Transformer (Multimodal ViT) extends the Vision Transformer architecture to jointly process and align representations from multiple modalities — typically images and text — using self-attention and cross-attention mechanisms. By learning shared or aligned embedding spaces across modalities, it enables tasks such as visual question answering, image-text retrieval, visual grounding, and image captioning. | The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs). |
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