เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| DETR (Detection Transformer)× | Swin Transformer (โปรแกรมแปลง Swin)× | วิชันแมมบ้า× | |
|---|---|---|---|
| สาขาวิชา | การเรียนรู้เชิงลึก | การเรียนรู้เชิงลึก | การเรียนรู้เชิงลึก |
| ตระกูล | Machine learning | Machine learning | Machine learning |
| ปีกำเนิด≠ | 2020 | 2021 | 2024 |
| ผู้ริเริ่ม≠ | Nicolas Carion | Ze Liu | Li Zhu |
| ประเภท | Neural network architecture | Neural network architecture | Neural network architecture |
| แหล่งต้นตำรับ≠ | 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 ↗ | 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 ↗ |
| ชื่อเรียกอื่น | Detection Transformer, DETR | Swin, Hierarchical Vision Transformer | ViM, Mamba for Vision |
| ที่เกี่ยวข้อง | 4 | 4 | 4 |
| สรุป≠ | 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. | 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. |
| ScholarGateชุดข้อมูล ↗ |
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