[26] S.-H. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. Torr. Res2net: A new multi-scale backbone architecture. IEEE transactions on pattern analysis and machine intelligence, 43(2):652-662, 2019.
[27] R. Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015.
[28] K. Gong, X. Liang, Y. Li, Y. Chen, M. Yang, and L. Lin. Instance-level human parsing via part grouping network. In Proceedings of the European conference on computer vision (ECCV), pages 770-785, 2018.
[29] K. Gong, X. Liang, D. Zhang, X. Shen, and L. Lin. Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 932-940, 2017.
[30] M.-H. Guo, C.-Z. Lu, Q. Hou, Z. Liu, M.-M. Cheng, and S.-M. Hu. Segnext: Rethinking convolutional attention design for semantic segmentation. arXiv preprint arXiv:2209.08575, 2022.
[31] J. He, Z. Deng, L. Zhou, Y. Wang, and Y. Qiao. Adaptive pyramid context network for semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7519-7528, 2019.
[32] K. He, G. Gkioxari, P. Dollar, and R. Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017.
[33] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[34] H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei. Relation networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3588-3597, 2018.
[35] X. Hu, K. Yang, L. Fei, and K. Wang. Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In 2019 IEEE International Conference on Image Processing (ICIP), pages 1440-1444. IEEE, 2019.
[36] L. Huang, Y. Yuan, J. Guo, C. Zhang, X. Chen, and J. Wang. Interlaced sparse self-attention for semantic segmentation. arXiv preprint arXiv:1907.12273, 2019.
[37] Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu. Ccnet: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 603-612, 2019.
[38] Z. Jin. Sssegmentation: An open source supervised semantic segmentation toolbox based on pytorch. arXiv preprint arXiv:2305.17091, 2023.
[39] Z. Jin, T. Gong, D. Yu, Q. Chu, J. Wang, C. Wang, and J. Shao. Mining contextual information beyond image for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7231-7241, 2021.
[40] Z. Jin, B. Liu, Q. Chu, and N. Yu. Isnet: Integrate image-level and semantic-level context for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7189-7198, 2021.
[41] Z. Jin, D. Yu, Z. Yuan, and L. Yu. Mcibi++: Soft mining contextual information beyond image for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
[42] T.-W. Ke, J.-J. Hwang, Z. Liu, and S. X. Yu. Adaptive affinity fields for semantic segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 587-602, 2018.
[43] T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
[44] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. nature, 521(7553):436-444, 2015.
[45] Y. LeCun, D. Touresky, G. Hinton, and T. Sejnowski. A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist models summer school, volume 1, pages 21-28, 1988.
[46] X. Li, Z. Zhong, J. Wu, Y. Yang, Z. Lin, and H. Liu. Expectation-maximization attention networks for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9167-9176, 2019.