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Transparent objects such as windows and bottles made by glass widely exist in the real world.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2003
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2014
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Transcut: Transparent object segmentation from a light-field image
Xu, Y., Nagahara, H., Shimada, A., Taniguchi, R.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Conditional random fields as recurrent neural networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: · 2015
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Efficient piecewise training of deep structured models for semantic segmentation
Lin, G., Shen, C., Van Den Hengel, A., Reid, I.: · 2016
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Semantic image segmentation with task-specific edge detection using cnns and a discriminatively trained domain transform
Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K., Yuille, A.L.: · 2016
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Superpixel convolutional networks using bilateral inceptions
Gadde, R., Jampani, V., Kiefel, M., Kappler, D., Gehler, P.V.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F., Navab, N., Ahmadi, S.A.: · 2016
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2017
Cited alongside, same era.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Lin, G., Milan, A., Shen, C., Reid, I.: · 2017
Cited alongside, same era.
Learning affinity via spatial propagation networks
Liu, S., De Mello, S., Gu, J., Zhong, G., Yang, M.H., Kautz, J.: · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: · 2017
Cited alongside, same era.
Tom-net: Learning transparent object matting from a single image
Chen, G., Han, K., Wong, K.K.: · 2018
Cited alongside, same era.
Icnet for real-time semantic segmentation on high-resolution images
Bisenet: Bilateral segmentation network for real-time semantic segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: · 2018
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Contextnet: Exploring context and detail for semantic segmentation in real-time
Poudel, R.P., Bonde, U., Liwicki, S., Zach, C.: · 2018
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Cgnet: A light-weight context guided network for semantic segmentation
Wu, T., Tang, S., Zhang, R., Zhang, Y.: · 2018
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Ocnet: Object context network for scene parsing
Yuan, Y., Wang, J.: · 2018
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Dunet: A deformable network for retinal vessel segmentation
Jin, Q., Meng, Z., Pham, T.D., Chen, Q., Wei, L., Su, R.: · 2019
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Feature pyramid encoding network for real-time semantic segmentation
Liu, M., Yin, H.: · 2019
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Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: · 2018
Cited alongside, same era.
Denseaspp for semantic segmentation in street scenes
Yang, M., Yu, K., Zhang, C., Li, Z., Yang, K.: · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: · 2018
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: · 2018
Cited alongside, same era.
Non-local neural networks
Wang, X., Girshick, R., Gupta, A., He, K.: · 2018
Cited alongside, same era.
The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., et al.: · 2018
Cited alongside, same era.
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Fast-scnn: fast semantic segmentation network
Poudel, R.P., Liwicki, S., Cipolla, R.: · 2019
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Deep high-resolution representation learning for visual recognition
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al.: · 2019
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Hardnet: A low memory traffic network
Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., Lin, Y.L.: · 2019
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Dabnet: Depth-wise asymmetric bottleneck for real-time semantic segmentation
Li, G., Yun, I., Kim, J., Kim, J.: · 2019
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Lednet: A lightweight encoder-decoder network for real-time semantic segmentation
Wang, Y., Zhou, Q., Liu, J., Xiong, J., Gao, G., Wu, X., Latecki, L.J.: · 2019
Later among the works it cites.