Comparer des méthodes

	Détection d'objets semi-supervisée	Segmentation d'instances
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2020–2021	2017
Auteur d'origine	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Type	Semi-supervised learning for detection	Pixel-level detection and mask prediction
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.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Alias	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Apparentées	6	4
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.	Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.