Machine learningDeep learning / NLP / CV
Semantic Segmentation
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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Sources
- 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: 10.1109/CVPR.2015.7298965 ↗
- Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. DOI: 10.1109/TPAMI.2017.2699184 ↗
Related methods
Referenced by
Domain-adaptive Instance SegmentationDomain-adaptive vision transformerExplainable Image ClassificationExplainable Instance SegmentationExplainable Object DetectionExplainable Semantic SegmentationExplainable Vision TransformerFine-Tuned Semantic SegmentationFine-Tuned Vision TransformerImage ClassificationInstance SegmentationMultilingual Semantic SegmentationMultimodal Instance SegmentationMultimodal Object DetectionMultimodal Semantic SegmentationObject DetectionSelf-supervised Instance SegmentationSelf-supervised Semantic SegmentationSemi-supervised Instance SegmentationSemi-supervised Semantic SegmentationTransfer Learning with Convolutional Neural NetworkTransfer Learning with Instance SegmentationWeakly supervised convolutional neural networkWeakly Supervised Instance SegmentationWeakly Supervised Semantic Segmentation