방법 비교

	Fully Convolutional Network (FCN)	Mask R-CNN: 픽셀 단위 마스크를 이용한 인스턴스 분할	ResNet (Residual Network)
분야	딥러닝	딥러닝	딥러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2015	2017	2016
창시자	Long, J.; Shelhamer, E.; Darrell, T.	Kaiming He et al. (FAIR)	He, K.; Zhang, X.; Ren, S.; Sun, J.
유형	Dense pixel-wise prediction convolutional network	Instance segmentation deep neural network	Deep Convolutional Neural Network with skip connections
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.	He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), 2980–2988.	He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.
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	ResNet, Residual Network, Deep Residual Learning, ResNet-50
관련	2	2	4
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.	ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.