Salīdzināt metodes

	Vāji uzraudzīta semantiskā segmentācija	Semantiskā segmentācija
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2016	2015
Autors	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational	Long, J., Shelhamer, E., & Darrell, T.
Tips	Pixel-level classification with image-level or coarse supervision	Dense prediction / pixel-wise classification
Pirmavots	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929.	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.
Citi nosaukumi	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Saistītās	4	5
Kopsavilkums	Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost.	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.