1 |
L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-decoder with Atrous Separable Convolution for Semantic Image Segmentation," Proceedings of the European Conference on Computer Vision, pp. 801-818, 2018.
|
2 |
G. Ghiasi and C. C. Fowlkes, "Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation," Proceedings of European Conference on Computer Vision, pp. 519-534, 2016.
|
3 |
G. Lin, A. Milan, C. Shen and I. Reid, "Refine Net: Multi-path Refinement Networks for High-Resolution Semantic Segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1925-1934, 2017.
|
4 |
K. Simonyan and A. Zisserman. "Very Deep Convolutional Networks for Large-scale Image Recognition," Proceedings of 3rd International Conference on Learning Representations, 2015.
|
5 |
K. He, X. Zhang, S. Ren., and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2018.
|
6 |
F. Chollet, "Xception: Deep learning with depthwise separable convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251-1258, 2017.
|
7 |
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, et al., "The Cityscapes Dataset for Semantic Urban Scene Understanding," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213-3223, 2016.
|
8 |
C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, "Bisenet: Bilateral Segmentation Network for Real-time Semantic Segmentation," Proceedings of the European Conference on Computer Vision, pp. 325-341, 2018.
|
9 |
P. Adam, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan et. al, "Pytorch:An imperative style, high-performance deep learning library," Proceedings of the Advances in Neural Information Processing Systems, pp. 8026- 8037, 2019.
|
10 |
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, No. 4, pp. 834-848, 2018.
DOI
|
11 |
H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia, "Pyramid scene parsing network," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881-2890, 2017.
|
12 |
J. Long, E. Shelhamer, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," Proceedings of the IEEE Conference on Computer Vision and P attern Recognition, pp. 3431-3440, 2015.
|
13 |
H. Noh, S. Hong, and B. Han, "Learning Deconvolution Network for Semantic Segmentation," Proceedings of the IEEE International Conference on Computer Vision, pp. 1520-1528, 2015.
|
14 |
O. Ronneberger, P. Fischer, and T. Brox, "Unet: Convolutional Networks for Biomedical Image Segmentation," Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234-241, 2015.
|
15 |
V. Badrinarayanan, A. Kendall, and R. Cipolla, "Segnet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, pp. 2481-2495, 2017.
DOI
|
16 |
S.K. Kim and C.W. Lee, "Segmentation Image Semantic Combining Image-level and Pixel-level Classification," Journal of Korea Multimedia Society, Vol. 21, No. 12, pp. 1425-1430, 2018.
DOI
|