Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Маскированные автокодировщики× | Swin Transformer× | Vision Mamba× | |
|---|---|---|---|
| Область | Глубокое обучение | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Год появления≠ | 2021 | 2021 | 2024 |
| Автор метода≠ | Kaiming He | Ze Liu | Li Zhu |
| Тип | Neural network architecture | Neural network architecture | Neural network architecture |
| Основополагающий источник≠ | He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000-16009). DOI ↗ | 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 ↗ | 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 ↗ |
| Другие названия | MAE, Vision MAE | Swin, Hierarchical Vision Transformer | ViM, Mamba for Vision |
| Связанные | 4 | 4 | 4 |
| Сводка≠ | Masked Autoencoders (MAE) is a self-supervised learning approach introduced by He et al. in 2021 that masks random patches of an image and trains a model to reconstruct the missing content. Adapting the masked language modeling paradigm from NLP to vision, MAE learns rich visual representations by solving a challenging reconstruction task without requiring labels. | 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. | 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. |
| ScholarGateНабор данных ↗ |
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