Machine learning
全卷积网络（FCN）
全卷积网络（FCN）由 Long、Shelhamer 和 Darrell 于 2015 年在 CVPR 上提出，是首个端到端深度学习架构，用于从任意尺寸的图像生成密集像素级语义分割图。通过用卷积层替换分类 CNN 的全连接层，并引入通过转置卷积和跳跃连接学习到的上采样，FCN 能够直接为图像中的每个像素预测类别标签，为后续所有分割架构（包括 U-Net 和 DeepLab）奠定了模板。
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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 ↗
- Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651. DOI: 10.1109/TPAMI.2016.2572683 ↗
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9). MIT Press. ISBN: 978-0-262-03561-3
如何引用本页
ScholarGate. (2026, June 3). Fully Convolutional Network for Semantic Segmentation. ScholarGate. https://scholargate.app/zh/deep-learning/fully-convolutional-network
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