เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| DETR (Detection Transformer)× | Swin Transformer (โปรแกรมแปลง Swin)× | วิชันทรานส์ฟอร์มเมอร์× | |
|---|---|---|---|
| สาขาวิชา | การเรียนรู้เชิงลึก | การเรียนรู้เชิงลึก | การเรียนรู้เชิงลึก |
| ตระกูล | Machine learning | Machine learning | Machine learning |
| ปีกำเนิด≠ | 2020 | 2021 | 2021 |
| ผู้ริเริ่ม≠ | Nicolas Carion | Ze Liu | Dosovitskiy, A. et al. |
| ประเภท≠ | Neural network architecture | Neural network architecture | Transformer architecture for images (self-attention over patches) |
| 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 ↗ | Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR. link ↗ |
| ชื่อเรียกอื่น≠ | Detection Transformer, DETR | Swin, Hierarchical Vision Transformer | Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images |
| ที่เกี่ยวข้อง≠ | 4 | 4 | 5 |
| 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. | 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). |
| ScholarGateชุดข้อมูล ↗ |
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