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Chen, \u201cSaliency detection based region extraction for pedestrian detection system with thermal imageries,\u201d IEICE Trans. Fundamentals, vol.E101-A, no.1, pp.306-310, 2018. 10.1587\/transfun.e101.a.306","DOI":"10.1587\/transfun.E101.A.306"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] H. Xiong, W. Cai, and Q. Liu, \u201cMCNet: Multi-level Correction Network for thermal image semantic segmentation of nighttime driving scene,\u201d Infrared Phys. Technol., vol.113, 103628, 2021. 10.1016\/j.infrared.2020.103628","DOI":"10.1016\/j.infrared.2020.103628"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] V. Badrinarayanan, A. Kendall, and R. Cipolla, \u201cSegnet: A deep convolutional encoder-decoder architecture for image segmentation,\u201d IEEE Trans Pattern Anal Mach Intell, vol.39, no.12, pp.2481-2495, 2017. 10.1109\/tpami.2016.2644615","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] L.C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, \u201cEncoder-decoder with atrous separable convolution for semantic image segmentation,\u201d Eur. Conf. Comput. Vis., pp.801-818, 2018.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, \u201c Pyramid scene parsing network,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.6230-6239, 2017. 10.1109\/cvpr.2017.660","DOI":"10.1109\/CVPR.2017.660"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] C. Li, W. Xia, Y. Yan, B. Luo, and J. Tang, \u201c Segmenting objects in day and night: Edge-conditioned cnn for thermal image semantic segmentation,\u201d IEEE Trans. Neural Netw. Learn, Syst., vol.32, no.7, pp.3069-3082, 2021. 10.1109\/tnnls.2020.3009373","DOI":"10.1109\/TNNLS.2020.3009373"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] G.-A. Bilodeau, A. Torabi, P.-L. St-Charles, and D. Riahi, \u201cThermal-visible registration of human silhouettes: A similarity measure performance evaluation,\u201d Infrared Phys. Technol., vol.64, pp.79-86, 2014. 10.1016\/j.infrared.2014.02.005","DOI":"10.1016\/j.infrared.2014.02.005"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. You, Y. Ji, Liu, S., C. Mei, X. Yao, and Y. Feng, \u201cA thermal infrared pedestrian-detection method for edge computing devices,\u201d Sensors, vol.22, no.17, 6710, 2022. 10.3390\/s22176710","DOI":"10.3390\/s22176710"},{"key":"12","unstructured":"[12] Z. Wu, C. Shen, and A.V.D. Hengel, \u201cReal-time semantic image segmentation via spatial sparsity,\u201d arXiv preprint arXiv:1712.00213, 2017"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] F. Chollet, \u201cXception: Deep learning with depthwise separable convolutions,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.1800-1807, 2017. 10.1109\/cvpr.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, \u201cBisenet: Bilateral segmentation network for real-time semantic segmentation,\u201d Eur. Conf. Comput. Vis., pp.325-341, 2018. 10.1007\/978-3-030-01261-8_20","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] Q. Ha, K. Watanabe, T. Karasawa, Y. Ushiku, and T. Harada, \u201cMFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes,\u201d 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.5108-5115, 2017. 10.1109\/iros.2017.8206396","DOI":"10.1109\/IROS.2017.8206396"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] P. Wang and X. Bai, \u201cThermal infrared pedestrian segmentation based on conditional GAN,\u201d IEEE Trans Image Process, vol.28, no.12, pp.6007-6021, 2019. 10.1109\/tip.2019.2924171","DOI":"10.1109\/TIP.2019.2924171"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J.W. Davis and M.A. Keck, \u201c A two-stage template approach to person detection in thermal imagery,\u201d WACV\/MOTION&apos;05, vol.1, pp.364-369, 2005. 10.1109\/acvmot.2005.14","DOI":"10.1109\/ACVMOT.2005.14"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] J. Long, E. Shelhamer, and T. Darrell, \u201cFully convolutional networks for semantic segmentation,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.3431-3440, 2015. 10.1109\/cvpr.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] O. Ronneberger, P. Fischer, and T. Brox, \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d MMICCAI, vol.9351, pp.234-241, 2015. 10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"20","unstructured":"[20] L.C. Chen, G. Papandreou, F. Schroff, and H. Adam, \u201cRethinking atrous convolution for semantic image segmentation,\u201d arXiv preprint arXiv:1706.05587, 2017."},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] K. Sun, B. Xiao, D. Liu, and J. Wang, \u201cDeep high-resolution representation learning for human pose estimation,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.5686-5696, 2019. 10.1109\/cvpr.2019.00584","DOI":"10.1109\/CVPR.2019.00584"},{"key":"22","unstructured":"[22] G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. 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Cipolla, \u201cFast-scnn: Fast semantic segmentation network,\u201d arXiv preprint arXiv:1902.04502, 2019."},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] C. Yu, C. Gao, J. Wang, G. Yu, C. Shen, and N. Sang, \u201cBisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation,\u201d Int. J. Comput. Vis., vol.129, no.11, pp.3051-3068, 2021. 10.1007\/s11263-021-01515-2","DOI":"10.1007\/s11263-021-01515-2"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] M. Fan, S. Lai, J. Huang, X. Wei, Z. Chai, J. Luo, and X. Wei, \u201c Rethinking BiSeNet for real-time semantic segmentation,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.9711-9720, 2021. 10.1109\/cvpr46437.2021.00959","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] J. Hu, L. Shen, and G. Sun, \u201cSqueeze-and-excitation networks,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.7132-7141, 2018. 10.1109\/cvpr.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] Q. Hou, D. Zhou, and J. Feng, \u201c Coordinate attention for efficient mobile network design,\u201d IEEE Conf. Comput. Vis. Pattern Recognit., pp.13708-13717, 2021. 10.1109\/cvpr46437.2021.01350","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] S. Woo, J. Park, J.-Y. Lee, and I.S. Kweon, \u201cCbam: Convolutional block attention module,\u201d Eur. Conf. Comput. Vis., vol.11211, pp.3-19, 2018. 10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"31","unstructured":"[31] L. Yang, R.Y. Zhang, L. Li, and X. Xie , \u201cSimam: A simple, parameter-free attention module for convolutional neural networks,\u201d International Conference on Machine Learning, PMLR, vol.139, pp.11863-11874, 2021."},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] S. Chen, Z. Chen, X. Xu, N. Yang, and X. He, \u201cNv-Net: Efficient infrared image segmentation with convolutional neural networks in the low illumination environment,\u201d Infrared Phys. Technol., vol.105, 103184, 2020. 10.1016\/j.infrared.2019.103184","DOI":"10.1016\/j.infrared.2019.103184"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] X. Qin, Z. Zhang, C. Huang, M. Dehghan, O.R. Zaiane, and M. 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