Machine learningDeep learning / NLP / CV
微调语义分割
微调语义分割是指将预训练在大型像素标注数据集上的深度神经网络（例如，在COCO或Cityscapes上训练的带有编码器-解码器头的ImageNet预训练骨干网络）通过在特定目标域的标注图像上继续训练，来适应新的目标域。其结果是，模型能够为图像中的每个像素分配一个类别标签，同时利用从远超目标域本身所能提供的数据中学到的丰富视觉表示。
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来源
- 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 ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Fine-Tuned Semantic Segmentation (Transfer Learning for Dense Pixel-wise Classification). ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-semantic-segmentation
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