Salīdzināt metodes

	Vāji uzraudzīta semantiskā segmentācija	Objektu noteikšana
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2016	2014–2016
Autors	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Tips	Pixel-level classification with image-level or coarse supervision	Supervised deep learning (region proposal or single-shot)
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.	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.
Citi nosaukumi	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification	visual object detection, image object localization, region-based object detection, bounding-box detection
Saistītās	4	3
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.	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.