方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 少样本目标检测× | Swin Transformer× | Vision Transformer× | |
|---|---|---|---|
| 领域 | 深度学习 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 2021 | 2021 |
| 提出者≠ | Xin Wang | Ze Liu | Dosovitskiy, A. et al. |
| 类型≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| 开创性文献≠ | Wang, X., Huang, T. E., Darrell, T., Gonzalez, J. E., & Yu, F. (2020). Few-shot object detection with attention-RPN and multi-relation detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9050-9059). 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 ↗ |
| 别名≠ | FSOD, Few-shot detection | Swin, Hierarchical Vision Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| 相关≠ | 3 | 4 | 5 |
| 摘要≠ | Few-Shot Object Detection (FSOD) is a meta-learning approach that enables detecting novel object classes from only a few annotated examples. Unlike standard object detection requiring hundreds of labeled instances per class, FSOD learns to quickly adapt detection models to new object categories by leveraging knowledge from base categories. | 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). |
| ScholarGate数据集 ↗ |
|
|
|