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| Mehrsprachiger Vision Transformer× | Multimodal Vision Transformer× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2021–2023 | 2021 |
| Urheber≠ | Dosovitskiy et al. (ViT base); multilingual extension by multiple groups (2021–2023) | Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT) |
| Typ≠ | Transformer-based vision model with multilingual capabilities | Multimodal transformer model |
| Wegweisende Quelle≠ | 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). link ↗ | 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 ↗ |
| Aliasnamen | Multilingual ViT, Cross-lingual Vision Transformer, Multilingual Visual Transformer, ML-ViT | Multimodal ViT, vision-language transformer, cross-modal vision transformer, multi-modal ViT |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. | 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. |
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