Linganisha mbinu

	Uchanganuzi wa Objekti kwa Njia ya Kudokezwa kwa Udhaifu (WSOD)	Uchanganuzi Semi-Nusu-Jitoleaji wa Vitu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2016 (deep WSOD); MIL roots circa 1997	2020–2021
Mwanzilishi	Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997)	Sohn et al. (STAC); Liu et al. (Unbiased Teacher)
Aina	Weakly supervised detection paradigm	Semi-supervised learning for detection
Chanzo asilia	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.	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.
Majina mbadala	WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection	SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection
Zinazohusiana	5	6
Muhtasari	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.	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.