Linganisha mbinu

	Uchanganuzi Semi-Nusu-Jitoleaji wa Vitu	Uchanganuzi wa Objekti kwa Njia ya Kudokezwa kwa Udhaifu (WSOD)
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2020–2021	2016 (deep WSOD); MIL roots circa 1997
Mwanzilishi	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)	Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997)
Aina	Semi-supervised learning for detection	Weakly supervised detection paradigm
Chanzo asilia	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.	Bilen, H., & Vedaldi, A. (2016). Weakly supervised deep detection networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2846–2854.
Majina mbadala	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection	WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection
Zinazohusiana	6	5
Muhtasari	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.	Weakly Supervised Object Detection (WSOD) trains object detectors using only image-level labels — indicating which object classes appear in an image — without requiring costly bounding-box annotations. Multiple Instance Learning (MIL) formulations allow the model to discover the likely location of each object class from classification signals alone, dramatically reducing annotation cost.