Comparer des méthodes

	Classification d'images	Segmentation sémantique
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2015
Auteur d'origine	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Long, J., Shelhamer, E., & Darrell, T.
Type	Supervised classification task	Dense prediction / pixel-wise classification
Source fondatrice	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.	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	visual classification, image recognition, CNN-based classification, visual categorization	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Apparentées	5	5
Résumé	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.	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.