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Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: · 2009
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The pascal visual object classes (voc) challenge
Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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Semantic contours from inverse detectors
Hariharan, B., Arbelaez, P., Bourdev, L., Maji, S., Malik, J.: · 2011
Earlier work this paper cites.
Efficient inference in fully connected crfs with gaussian edge potentials
Krähenbühl, P., Koltun, V.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
Earlier work this paper cites.
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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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Deeply-supervised nets
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: · 2014
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Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Dai, J., He, K., Sun, J.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Cited alongside, same era.
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.: · 2016
Cited alongside, same era.
Laplacian pyramid reconstruction and refinement for semantic segmentation
Ghiasi, G., Fowlkes, C.C.: · 2016
Cited alongside, same era.
Pyramid scene parsing network
Feature pyramid networks for object detection
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2016
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Wider or deeper: Revisiting the resnet model for visual recognition
Wu, Z., Shen, C., Hengel, A.V.D.: · 2016
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Segnet: A deep convolutional encoder-decoder architecture for scene segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2017
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Rethinking atrous convolution for semantic image segmentation
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: · 2017
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Gated feedback refinement network for dense image labeling
Amirul Islam, M., Rochan, M., Bruce, N.D.B., Wang, Y.: · 2017
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Large kernel matters – improve semantic segmentation by global convolutional network
Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.: · 2017
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2016
Cited alongside, same era.
Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Lin, G., Milan, A., Shen, C., Reid, I.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2016
Cited alongside, same era.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: · 2016
Cited alongside, same era.
Full-resolution residual networks for semantic segmentation in street scenes
Pohlen, T., Hermans, A., Mathias, M., Leibe, B.: · 2016
Cited alongside, same era.
Later among the works it cites.
Understanding convolution for semantic segmentation
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.: · 2017
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Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Aitken, A., Ledig, C., Theis, L., Caballero, J., Wang, Z., Shi, W.: · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: · 2017
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Squeeze-and-excitation networks
Hu, J., Shen, L., Sun, G.: · 2017
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Convolutional neural pyramid for image processing
Shen, X., Chen, Y.C., Tao, X., Jia, J.: · 2017
Later among the works it cites.
Stacked deconvolutional network for semantic segmentation
Fu, J., Liu, J., Wang, Y., Lu, H.: · 2017
Later among the works it cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: · 2018
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