قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| المحول البصري القابل للتفسير (Explainable Vision Transformer)× | المُحوِّل البصري متعدد الوسائط (Multimodal ViT)× | |
|---|---|---|
| المجال | التعلم العميق | التعلم العميق |
| العائلة | Machine learning | Machine learning |
| سنة النشأة | 2021 | 2021 |
| صاحب الطريقة≠ | Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT) | Dosovitskiy et al. (ViT); Radford et al. (CLIP multimodal ViT) |
| النوع≠ | Post-hoc explainability applied to Vision Transformer | Multimodal transformer model |
| المصدر التأسيسي≠ | Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791. DOI ↗ | 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 ↗ |
| الأسماء البديلة | XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer | Multimodal ViT, vision-language transformer, cross-modal vision transformer, multi-modal ViT |
| ذات صلة | 5 | 5 |
| الملخص≠ | Explainable Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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