Comparer des méthodes

	Segmentation d'instances faiblement supervisée	Segmentation sémantique
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2015–2019	2015
Auteur d'origine	Multiple contributors (e.g., Hsu et al., Khoreva et al.)	Long, J., Shelhamer, E., & Darrell, T.
Type	Weakly supervised deep learning for pixel-wise instance delineation	Dense prediction / pixel-wise classification
Source fondatrice	Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32.	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.
Alias	WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Apparentées	6	5
Résumé	Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image.	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.