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| Wykrywanie obiektów z niewielu przykładów× | DETR (Detection Transformer)× | Swin Transformer× | |
|---|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning | Machine learning |
| Rok powstania≠ | 2020 | 2020 | 2021 |
| Twórca≠ | Xin Wang | Nicolas Carion | Ze Liu |
| Typ | Neural network architecture | Neural network architecture | Neural network architecture |
| Źródło pierwotne≠ | 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 ↗ | Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. In European Conference on Computer Vision (pp. 213-229). Springer, Cham. 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 ↗ |
| Inne nazwy | FSOD, Few-shot detection | Detection Transformer, DETR | Swin, Hierarchical Vision Transformer |
| Pokrewne≠ | 3 | 4 | 4 |
| Podsumowanie≠ | 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. | DETR (Detection Transformer) is an end-to-end framework for object detection introduced by Carion et al. in 2020 that reformulates detection as a direct set prediction problem using transformers. Unlike traditional approaches that use hand-crafted post-processing like non-maximum suppression, DETR treats object detection as a sequence-to-sequence problem where the transformer predicts all objects at once. | 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. |
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