Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Pooljärelevalvega objektituvastus× | Objektituvastus× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2020–2021 | 2014–2016 |
| Looja≠ | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| Tüüp≠ | Semi-supervised learning for detection | Supervised deep learning (region proposal or single-shot) |
| Algallikas≠ | 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 ↗ |
| Rööpnimetused | 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 |
| Seotud≠ | 6 | 3 |
| Kokkuvõte≠ | 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. |
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