Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Semi-overvåket objektdeteksjon× | Objektdeteksjon× | |
|---|---|---|
| Fagfelt | Dyp læring | Dyp læring |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2020–2021 | 2014–2016 |
| Opphavsperson≠ | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| Type≠ | Semi-supervised learning for detection | Supervised deep learning (region proposal or single-shot) |
| Opprinnelig kilde≠ | Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757. link ↗ | 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 ↗ |
| Alias | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection | visual object detection, image object localization, region-based object detection, bounding-box detection |
| Relaterte≠ | 6 | 3 |
| Sammendrag≠ | Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines. | 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. |
| ScholarGateDatasett ↗ |
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