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| 객체 탐지를 위한 전이 학습× | 객체 탐지× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2010–2014 | 2014–2016 |
| 창시자≠ | Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| 유형≠ | Transfer learning / fine-tuning | Supervised deep learning (region proposal or single-shot) |
| 원전≠ | Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587. DOI ↗ |
| 별칭 | pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection | visual object detection, image object localization, region-based object detection, bounding-box detection |
| 관련 | 3 | 3 |
| 요약≠ | Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require. | Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks. |
| ScholarGate데이터셋 ↗ |
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