方法对比
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| 全卷积网络（FCN）× | Mask R-CNN：具有像素级掩码的实例分割× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015 | 2017 |
| 提出者≠ | Long, J.; Shelhamer, E.; Darrell, T. | Kaiming He et al. (FAIR) |
| 类型≠ | Dense pixel-wise prediction convolutional network | Instance segmentation deep neural network |
| 开创性文献≠ | 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 ↗ | He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2980–2988. DOI ↗ |
| 别名≠ | FCN, fully convolutional network, FCN-32s, FCN-16s | Mask Region-based Convolutional Neural Network, Instance Segmentation R-CNN, He et al. 2017 Segmentation Model, Maske R-CNN |
| 相关 | 2 | 2 |
| 摘要≠ | The Fully Convolutional Network (FCN), introduced by Long, Shelhamer, and Darrell at CVPR 2015, was the first end-to-end deep learning architecture trained to produce dense pixel-wise semantic segmentation maps from images of arbitrary size. By replacing the fully connected layers of a classification CNN with convolutional layers and adding learned upsampling through transposed convolutions and skip connections, FCN enabled the direct prediction of a class label for every pixel in an image, establishing the template for all subsequent segmentation architectures including U-Net and DeepLab. | Mask R-CNN is a deep learning framework for instance segmentation introduced by Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick at Facebook AI Research (FAIR) in 2017. It extends Faster R-CNN by adding a parallel branch that predicts a binary pixel-level mask for each detected object instance, enabling simultaneous object detection, classification, and fine-grained segmentation in a single forward pass. |
| ScholarGate数据集 ↗ |
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