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	Classificazione multilingue delle immagini	Vision Transformer Multilingue
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2020s	2021–2023
Ideatore	Community / Radford et al. (CLIP, 2021) as key enabler	Dosovitskiy et al. (ViT base); multilingual extension by multiple groups (2021–2023)
Tipo	Cross-lingual supervised image classification	Transformer-based vision model with multilingual capabilities
Fonte seminale	Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR.	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).
Alias	Cross-lingual image classification, Multilingual visual recognition, Cross-cultural image classification, Multilingual vision-language classification	Multilingual ViT, Cross-lingual Vision Transformer, Multilingual Visual Transformer, ML-ViT
Correlati	5	4
Sintesi	Multilingual image classification trains visual models to recognise and label images when class names, supervision signals, or evaluation benchmarks span multiple languages. Enabled by multilingual vision-language models such as CLIP, it allows a single model to classify images using prompts or labels in any supported language, facilitating cross-cultural and cross-lingual deployment of computer vision systems.	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.