قارن الطرق

	اكتشاف الكائنات شبه المُشرف عليه	تقسيم الكيانات (Instance Segmentation)
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2020–2021	2017
صاحب الطريقة	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	He, K., Gkioxari, G., Dollar, P., Girshick, R.
النوع	Semi-supervised learning for detection	Pixel-level detection and mask prediction
المصدر التأسيسي	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.
الأسماء البديلة	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
ذات صلة	6	4
الملخص	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.