Machine learningDeep learning / NLP / CV

Fino podešena semantička segmentacija

Fino podešena semantička segmentacija prilagođava duboku neuronsku mrežu prethodno obučenu na velikom skupu podataka sa etiketiranim pikselima (npr. osnova obučena na ImageNet-u sa enkoder-dekoder glavom obučenom na COCO ili Cityscapes) na novu ciljnu domen primenom nastavka obučavanja na slikama sa anotacijama specifičnim za domen. Rezultat je model koji dodeljuje oznaku klase svakom pikselu u slici, istovremeno koristeći bogate vizuelne reprezentacije naučene iz znatno više podataka nego što bi ciljni domen sam mogao da pruži.

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Izvori

  1. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440. DOI: 10.1109/CVPR.2015.7298965
  2. 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

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Fine-Tuned Semantic Segmentation (Transfer Learning for Dense Pixel-wise Classification). ScholarGate. https://scholargate.app/sr/deep-learning/fine-tuned-semantic-segmentation

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Citirana u

ScholarGateFine-Tuned Semantic Segmentation (Fine-Tuned Semantic Segmentation (Transfer Learning for Dense Pixel-wise Classification)). Preuzeto 2026-06-15 sa https://scholargate.app/sr/deep-learning/fine-tuned-semantic-segmentation · Skup podataka: https://doi.org/10.5281/zenodo.20539026