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Semantic segmentation is an important task for numerous applications but it is still quite challenging to achieve advanced performance with limited computational costs.
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2014
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Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR. pp. 3431–3440 (2015)
2015
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Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE T-PAMI 40
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR. pp. 2881–2890 (2017)
2017
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Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: CVPR. pp. 633–641 (2017)
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Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: Thing and stuff classes in context. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1209–1218 (2018)
2018
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Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: Thing and stuff classes in context. In: CVPR. pp. 1209–1218 (2018)
2018
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Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV. pp. 801–818 (2018)
2018
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Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV). pp. 801–818 (2018)
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Hu, J., Shen, L., Albanie, S., Sun, G., Vedaldi, A.: Gather-excite: Exploiting feature context in convolutional neural networks. Advances in neural information processing systems 31
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Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR. pp. 7794–7803 (2018)
2018
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Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: Bilateral segmentation network for real-time semantic segmentation. In: ECCV. pp. 325–341 (2018)
2018
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Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., Agrawal, A.: Context encoding for semantic segmentation. In: CVPR. pp. 7151–7160 (2018)
2018
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Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In: ICCV Workshops. pp. 0–0 (2019)
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Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: CVPR. pp. 3146–3154 (2019)
2019
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Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: Ccnet: Criss-cross attention for semantic segmentation. In: ICCV. pp. 603–612 (2019)
2019
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Li, H., Xiong, P., Fan, H., Sun, J.: Dfanet: Deep feature aggregation for real-time semantic segmentation. In: CVPR. pp. 9522–9531 (2019)
2019
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Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H.: Expectation-maximization attention networks for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9167–9176 (2019)
2019
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Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., Fei-Fei, L.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In: CVPR. pp. 82–92 (2019)
2019
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Bousselham, W., Thibault, G., Pagano, L., Machireddy, A., Gray, J., Chang, Y.H., Song, X.: Efficient self-ensemble for semantic segmentation. British Machine Vision Conference (2022)
2022
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Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1290–1299 (2022)
2022
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Guo, M.H., Lu, C.Z., Hou, Q., Liu, Z.N., Cheng, M.M., Hu, S.m.: Segnext: Rethinking convolutional attention design for semantic segmentation. In: NeurIPS (2022)
2022
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2022
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Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., Xiao, B.: Deep high-resolution representation learning for visual recognition. TPAMI (2019)
2019
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Contributors, M.: MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation (2020)
2020
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Yuan, Y., Chen, X., Wang, J.: Object-contextual representations for semantic segmentation. In: ECCV. pp. 173–190 (2020)
2020
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Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. NeurIPS 34
2021
Cited alongside, same era.
Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J., Wei, X.: Rethinking bisenet for real-time semantic segmentation. In: CVPR. pp. 9716–9725 (2021)
2021
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2021
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Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13713–13722 (2021)
2021
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Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., Van Gool, L.: Exploring cross-image pixel contrast for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 7303–7313 (2021)
2021
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Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: CVPR. pp. 11976–11986 (2022)
2022
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Zhang, B., Tian, Z., Tang, Q., Chu, X., Wei, X., Shen, C., et al.: Segvit: Semantic segmentation with plain vision transformers pp. 4971–4982 (2022)
2022
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Zhang, W., Huang, Z., Luo, G., Chen, T., Wang, X., Liu, W., Yu, G., Shen, C.: Topformer: Token pyramid transformer for mobile semantic segmentation. In: CVPR. pp. 12083–12093 (2022)
2022
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Zhou, T., Wang, W., Konukoglu, E., Van Gool, L.: Rethinking semantic segmentation: A prototype view. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2582–2593 (2022)
2022
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Li, L., Wang, W., Zhou, T., Quan, R., Yang, Y.: Semantic hierarchy-aware segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
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2023
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Shim, J.h., Yu, H., Kong, K., Kang, S.J.: Feedformer: Revisiting transformer decoder for efficient semantic segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 37, pp. 2263–2271 (2023)
2023
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Wan, Q., Huang, Z., Lu, J., Yu, G., Zhang, L.: Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation (2023)
2023
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2023
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2023
Later among the works it cites.
Cavagnero, N., Rosi, G., Ruttano, C., Pistilli, F., Ciccone, M., Averta, G., Cermelli, F.: Pem: Prototype-based efficient maskformer for image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (2024)
2024
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