手法を比較

	マルチモーダルVision Transformer	ビジョントランスフォーマー
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2021	2021
提唱者	Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT)	Dosovitskiy, A. et al.
種類	Multimodal transformer model	Transformer architecture for images (self-attention over patches)
原典	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).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
別名	Multimodal ViT, vision-language transformer, cross-modal vision transformer, multi-modal ViT	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
関連	5	5
概要	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).