Comparer des méthodes

	Détection d'objets semi-supervisée	Détection d'objets
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2020–2021	2014–2016
Auteur d'origine	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)
Source fondatrice	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.	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.
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
Apparentées	6	3
Résumé	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.