So sánh phương pháp

	Transformer Thị giác Đa ngôn ngữ	Transformer Thị giác
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2021–2023	2021
Người khởi xướng	Dosovitskiy et al. (ViT base); multilingual extension by multiple groups (2021–2023)	Dosovitskiy, A. et al.
Loại	Transformer-based vision model with multilingual capabilities	Transformer architecture for images (self-attention over patches)
Công trình gốc	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. International Conference on Learning Representations (ICLR 2021).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Tên gọi khác	Multilingual ViT, Cross-lingual Vision Transformer, Multilingual Visual Transformer, ML-ViT	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Liên quan	4	5
Tóm tắt	Multilingual Vision Transformer (Multilingual ViT) extends the Vision Transformer architecture to operate across multiple languages, enabling image understanding and image-text reasoning in multilingual or cross-lingual settings. It combines patch-based image encoding with multilingual text representations, allowing a single model to serve diverse linguistic communities for tasks such as image captioning, visual question answering, and cross-lingual image retrieval.	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).