Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Vision Mamba× | Swin Transformer× | Трансформер для комп'ютерного зору× | |
|---|---|---|---|
| Галузь | Глибоке навчання | Глибоке навчання | Глибоке навчання |
| Родина | Machine learning | Machine learning | Machine learning |
| Рік появи≠ | 2024 | 2021 | 2021 |
| Автор методу≠ | Li Zhu | Ze Liu | Dosovitskiy, A. et al. |
| Тип≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| Основоположне джерело≠ | Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning. link ↗ | Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 10012-10022). DOI ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| Інші назви≠ | ViM, Mamba for Vision | Swin, Hierarchical Vision Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| Пов'язані≠ | 4 | 4 | 5 |
| Підсумок≠ | Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity. | The Swin Transformer is a hierarchical vision transformer introduced by Liu et al. in 2021 that uses shifted window attention to achieve computational efficiency while maintaining strong performance on computer vision tasks. Unlike the original Vision Transformer which applies global self-attention, Swin uses local window-based attention with periodic shifting to balance expressiveness and efficiency. | 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). |
| ScholarGateНабір даних ↗ |
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