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Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by multi-scale feature fusion.
Semantic object classes in video: A high-definition ground truth database
Brostow, G.J., Fauqueur, J., Cipolla, R.: · 2008
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas, G., Philip, L., Raquel, U.: · 2012
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Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: · 2014
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An exemplar-based crf for multi-instance object segmentation
He, X., Gould, S.: · 2014
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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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.: · 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.S.: · 2015
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Semantic image segmentation via deep parsing network
Liu, Z., Li, X., Luo, P., Loy, C.C., Tang, X.: · 2015
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Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: · 2015
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: · 2015
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Understanding the effective receptive field in deep convolutional neural networks
Luo, W., Li, Y., Urtasun, R., Zemel, R.: · 2016
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Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 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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Semantic segmentation with boundary neural fields
Bertasius, G., Shi, J., Torresani, L.: · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
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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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Learning sparse high dimensional filters: Image filtering, dense crfs and bilateral neural networks
Jampani, V., Kiefel, M., Gehler, P.V.: · 2016
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Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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A benchmark dataset and evaluation methodology for video object segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: · 2016
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Wide residual networks
Zagoruyko, S., Komodakis, N.: · 2016
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 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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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T.N., Welling, M.: · 2017
Cited alongside, same era.
Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: · 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.
Convolutional random walk networks for semantic image segmentation
Bertasius, G., Torresani, L., Yu, S.X., Shi, J.: · 2017
Cited alongside, same era.
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I.: · 2017
In-place activated batchnorm for memory-optimized training of dnns
Rota Bulò, S., Porzi, L., Kontschieder, P.: · 2018
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Dense decoder shortcut connections for single-pass semantic segmentation
Bilinski, P., Prisacariu, V.: · 2018
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Deep spatio-temporal random fields for efficient video segmentation
Chandra, S., Couprie, C., Kokkinos, I.: · 2018
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Training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation
Meletis, P., Dubbelman, G.: · 2018
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mmdetection
Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., Ouyang, W., Loy, C.C., Lin, D.: · 2018
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Ccnet: Criss-cross attention for semantic segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: · 2019
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Cited alongside, same era.
Deep feature flow for video recognition
Zhu, X., Xiong, Y., Dai, J., Yuan, L., Wei, Y.: · 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.
The mapillary vistas dataset for semantic understanding of street scenes
Neuhold, G., Ollmann, T., Rota Bulo, S., Kontschieder, P.: · 2017
Cited alongside, same era.
Ladder-style densenets for semantic segmentation of large natural images
Krapac, J., Kreso, I., Segvic, S.: · 2017
Cited alongside, same era.
High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: · 2018
Cited alongside, same era.
Dual attention network for scene segmentation
Fu, J., Liu, J., Tian, H., Fang, Z., Lu, H.: · 2018
Cited alongside, same era.
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Expectation-maximization attention networks for semantic segmentation
Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H.: · 2019
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Dynamic multi-scale filters for semantic segmentation
He, J., Deng, Z., Qiao, Y.: · 2019
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Global aggregation then local distribution in fully convolutional networks
Li, X., Zhang, L., You, A., Yang, M., Yang, K., Tong, Y.: · 2019
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Dual graph convolutional network for semantic segmentation
Zhang, L., Li, X., Arnab, A., Yang, K., Tong, Y., Torr, P.H.: · 2019
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Gated-scnn: Gated shape cnns for semantic segmentation
Takikawa, T., Acuna, D., Jampani, V., Fidler, S.: · 2019
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Improving semantic segmentation via video propagation and label relaxation
Zhu, Y., Sapra, K., Reda, F.A., Shih, K.J., Newsam, S., Tao, A., Catanzaro, B.: · 2019
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Adaptive pyramid context network for semantic segmentation
He, J., Deng, Z., Zhou, L., Wang, Y., Qiao, Y.: · 2019
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Asymmetric non-local neural networks for semantic segmentation
Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: · 2019
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Boundary-aware feature propagation for scene segmentation
Ding, H., Jiang, X., Liu, A.Q., Thalmann, N.M., Wang, G.: · 2019
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Acfnet: Attentional class feature network for semantic segmentation
Zhang, F., Chen, Y., Li, Z., Hong, Z., Liu, J., Ma, F., Han, J., Ding, E.: · 2019
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Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A., Fei-Fei, L.: · 2019
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Strip pooling: Rethinking spatial pooling for scene parsing
Hou, Q., Zhang, L., Cheng, M.M., Feng, J.: · 2020
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Dynamic graph message passing networks
Zhang, L., Xu, D., Arnab, A., Torr, P.H.: · 2020
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Bdd100k: A diverse driving dataset for heterogeneous multitask learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., Darrell, T.: · 2020
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Spatial pyramid based graph reasoning for semantic segmentation
Li, X., Yang, Y., Zhao, Q., Shen, T., Lin, Z., Liu, H.: · 2020
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Gff: Gated fully fusion for semantic segmentation
Li, X., Houlong, Z., Lei, H., Yunhai, T., Kuiyuan, Y.: · 2020
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Fasterseg: Searching for faster real-time semantic segmentation
Chen, W., Gong, X., Liu, X., Zhang, Q., Li, Y., Wang, Z.: · 2020
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