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| Kuvanluokittelu× | Semanttinen segmentointi× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2012 (deep CNN era); conceptual roots 1989 (LeCun) | 2015 |
| Kehittäjä≠ | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. | Long, J., Shelhamer, E., & Darrell, T. |
| Tyyppi≠ | Supervised classification task | Dense prediction / pixel-wise classification |
| Alkuperäislähde≠ | 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. link ↗ | 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. DOI ↗ |
| Rinnakkaisnimet | visual classification, image recognition, CNN-based classification, visual categorization | pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. |
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