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| Fully Convolutional Network (FCN)× | Mask R-CNN: 픽셀 단위 마스크를 이용한 인스턴스 분할× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015 | 2017 |
| 창시자≠ | Long, J.; Shelhamer, E.; Darrell, T. | Kaiming He et al. (FAIR) |
| 유형≠ | Dense pixel-wise prediction convolutional network | Instance segmentation deep neural network |
| 원전≠ | 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 ↗ |
| 별칭≠ | 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 |
| 관련 | 2 | 2 |
| 요약≠ | 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. |
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