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Yang, Y. Yoshimura, A. Morita, T. Namiki, and T. Nakaguchi, \u201cPyramid predictive attention network for medical image segmentation,\u201d IEICE Trans. Fundamentals, vol.E102-A, no.9, pp.1225-1234, Setp. 2019. 10.1587\/transfun.e102.a.1225","DOI":"10.1587\/transfun.E102.A.1225"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] M.D. Sulistiyo, Y. Kawanishi, D. Deguchi, I. Ide, T. Hirayama, J.Y. Zheng, and H. Murase, \u201cAttribute-aware loss function for accurate semantic segmentation considering the pedestrian orientations,\u201d IEICE Trans. Fundamentals, vol.E103-A, no.1, pp.231-242, Jan. 2020. 10.1587\/transfun.2019tsp0001","DOI":"10.1587\/transfun.2019TSP0001"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] G. Bertasius, L. Torresani, S.X. Yu, and J. Shi, \u201cConvolutional random walk networks for semantic image segmentation,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6137-6145, 2017. 10.1109\/cvpr.2017.650","DOI":"10.1109\/CVPR.2017.650"},{"key":"4","unstructured":"[4] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, \u201cSemantic image segmentation with deep convolutional nets and fully connected CRFs,\u201d International Conference on Learning Representations (ICLR), 2015."},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, \u201cDeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.40, no.4, pp.834-848, 2018. 10.1109\/tpami.2017.2699184","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Ahn and S. Kwak, \u201cLearning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4981-4990, 2018. 10.1109\/cvpr.2018.00523","DOI":"10.1109\/CVPR.2018.00523"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] A. Khoreva, R. Benenson, J. Hosang, M. Hein, and B. Schiele, \u201cSimple does it: Weakly supervised instance and semantic segmentation,\u201d Conference on Computer Vision and Pattern Recognition (CVPR), pp.1665-1674, 2017. 10.1109\/cvpr.2017.181","DOI":"10.1109\/CVPR.2017.181"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] J. Lee, E. Kim, S. Lee, J. Lee, and S. Yoon, \u201cFickleNet: Weakly and semi-supervised semantic image segmentation using stochastic inference,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5267-5276, 2019. 10.1109\/cvpr.2019.00541","DOI":"10.1109\/CVPR.2019.00541"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] A. Chattopadhay, A. Sarkar, P. Howlader, and V.N. Balasubramanian, \u201cGrad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks,\u201d 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.839-847, 2018. 10.1109\/wacv.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, \u201cGrad-CAM: Visual explanations from deep networks via gradient-based localization,\u201d Proc. IEEE International Conference on Computer Vision, pp.618-626, 2017. 10.1109\/iccv.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, \u201cLearning deep features for discriminative localization,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2921-2929, 2016. 10.1109\/cvpr.2016.319","DOI":"10.1109\/CVPR.2016.319"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] Z. Huang, X. Wang, J. Wang, W. Liu, and J. Wang, \u201cWeakly-supervised semantic segmentation network with deep seeded region growing,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.7014-7023, 2018. 10.1109\/cvpr.2018.00733","DOI":"10.1109\/CVPR.2018.00733"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] K. Li, Z. Wu, K.C. Peng, J. Ernst, and Y. Fu, \u201cTell me where to look: Guided attention inference network,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.9215-9223, 2018. 10.1109\/cvpr.2018.00960","DOI":"10.1109\/CVPR.2018.00960"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] K.K. Singh and Y.J. Lee, \u201cHide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization,\u201d 2017 IEEE International Conference on Computer Vision (ICCV), pp.3544-3553, 2017. 10.1109\/iccv.2017.381","DOI":"10.1109\/ICCV.2017.381"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] Y. Wei, J. Feng, X. Liang, M.M. Cheng, Y. Zhao, and S. Yan, \u201cObject region mining with adversarial erasing: A simple classification to semantic segmentation approach,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1568-1576, 2017. 10.1109\/cvpr.2017.687","DOI":"10.1109\/CVPR.2017.687"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] Y. Wei, H. Xiao, H. Shi, Z. Jie, J. Feng, and T.S. Huang, \u201cRevisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.7268-7277, 2018. 10.1109\/cvpr.2018.00759","DOI":"10.1109\/CVPR.2018.00759"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] A. Kolesnikov and C.H. Lampert, \u201cSeed, expand and constrain: Three principles for weakly-supervised image segmentation,\u201d European Conference on Computer Vision (ECCV), pp.695-711, 2016.","DOI":"10.1007\/978-3-319-46493-0_42"},{"key":"18","unstructured":"[18] P. Kr\u00e4henb\u00fchl and V. Koltun, \u201cEfficient inference in fully connected CRFs with Gaussian edge potentials,\u201d Advances in Neural Information Processing Systems, pp.109-117, 2011."},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] Z. Chen and Q. Sun, \u201cExtracting class activation maps from non-discriminative features as well,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.3135-3144, 2023. 10.1109\/cvpr52729.2023.00306","DOI":"10.1109\/CVPR52729.2023.00306"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] T. Zhou, P. Krahenbuhl, M. Aubry, Q. Huang, and A.A. Efros, \u201cLearning dense correspondence via 3D-guided cycle consistency,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.117-126, 2016. 10.1109\/cvpr.2016.20","DOI":"10.1109\/CVPR.2016.20"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] T. Zhou, Y. Jae Lee, S.X. Yu, and A.A. Efros, \u201cFlowWeb: Joint image set alignment by weaving consistent, pixel-wise correspondences,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1191-1200, 2015. 10.1109\/cvpr.2015.7298723","DOI":"10.1109\/CVPR.2015.7298723"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] D. Dwibedi, Y. Aytar, J. Tompson, P. Sermanet, and A. Zisserman, \u201cTemporal cycle-consistency learning,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.1801-1810, 2019. 10.1109\/cvpr.2019.00190","DOI":"10.1109\/CVPR.2019.00190"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] F. Wang, Q. Huang, and L.J. Guibas, \u201cImage co-segmentation via consistent functional maps,\u201d Proc. IEEE International Conference on Computer Vision, pp.849-856, 2013. 10.1109\/iccv.2013.110","DOI":"10.1109\/ICCV.2013.110"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] F. Wang, Q. Huang, M. Ovsjanikov, and L.J. Guibas, \u201cUnsupervised multi-class joint image segmentation,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3142-3149, 2014. 10.1109\/cvpr.2014.402","DOI":"10.1109\/CVPR.2014.402"},{"key":"25","unstructured":"[25] J. Hoffman, E. Tzeng, T. Park, J.Y. Zhu, P. Isola, K. Saenko, A.A. Efros, and T. Darrell, \u201cCyCADA: Cycle-consistent adversarial domain adaptation,\u201d arXiv preprint arXiv:1711.03213, 2017. 10.48550\/arXiv.1711.03213"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] Y. Wang, J. Zhang, M. Kan, S. Shan, and X. Chen, \u201cSelf-supervised scale equivariant network for weakly supervised semantic segmentation,\u201d arXiv preprint arXiv:1909.03714, 2019. 10.48550\/arXiv.1909.03714","DOI":"10.1109\/CVPR42600.2020.01229"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] M. Everingham, L. Van Gool, C.K. Williams, J. Winn, and A. Zisserman, \u201cThe pascal visual object classes (VOC) challenge,\u201d Int. J. Comput. Vis., vol.88, no.2, pp.303-338, 2010. 10.1007\/s11263-009-0275-4","DOI":"10.1007\/s11263-009-0275-4"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] Z. Wu, C. Shen, and A. Van Den Hengel, \u201cWider or deeper: Revisiting the ResNet model for visual recognition,\u201d Pattern Recognition, vol.90, pp.119-133, 2019. 10.1016\/j.patcog.2019.01.006","DOI":"10.1016\/j.patcog.2019.01.006"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei, \u201cWhat's the point: Semantic segmentation with point supervision,\u201d European Conference on Computer Vision, pp.549-565, Springer, 2016. 10.1007\/978-3-319-46478-7_34","DOI":"10.1007\/978-3-319-46478-7_34"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] P. Vernaza and M. Chandraker, \u201cLearning random-walk label propagation for weakly-supervised semantic segmentation,\u201d Conference on Computer Vision and Pattern Recognition (CVPR), pp.2953-2961, 2017.","DOI":"10.1109\/CVPR.2017.315"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] D. Lin, J. Dai, J. Jia, K. He, and J. Sun, \u201cScribbleSup: Scribble-supervised convolutional networks for semantic segmentation,\u201d Conference on Computer Vision and Pattern Recognition (CVPR), pp.3159-3167, 2016. 10.1109\/cvpr.2016.344","DOI":"10.1109\/CVPR.2016.344"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] J. Dai, K. He, and J. Sun, \u201cBoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,\u201d IEEE International Conference on Computer Vision (ICCV), pp.1635-1643, 2015. 10.1109\/iccv.2015.191","DOI":"10.1109\/ICCV.2015.191"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] G. Papandreou, L.C. Chen, K.P. Murphy, and A.L. Yuille, \u201cWeakly-and semi-supervised learning of a DCNN for semantic image segmentation,\u201d IEEE International Conference on Computer Vision (ICCV), pp.1742-1750, 2015. 10.1109\/iccv.2015.203","DOI":"10.1109\/ICCV.2015.203"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] Y. Wei, X. Liang, Y. Chen, X. Shen, M.M. Cheng, J. Feng, Y. Zhao, and S. Yan, \u201cSTC: A simple to complex framework for weakly-supervised semantic segmentation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.39, no.11, pp.2314-2320, 2016. 10.1109\/tpami.2016.2636150","DOI":"10.1109\/TPAMI.2016.2636150"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] Y. Wei, J. Feng, X. Liang, M.M. Cheng, Y. Zhao, and S. Yan, \u201cObject region mining with adversarial erasing: A simple classification to semantic segmentation approach,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6488-6496, 2017. 10.1109\/cvpr.2017.687","DOI":"10.1109\/CVPR.2017.687"},{"key":"36","unstructured":"[36] Q. Hou, P. Jiang, Y. Wei, and M.M. Cheng, \u201cSelf-erasing network for integral object attention,\u201d Advances in Neural Information Processing Systems, pp.549-559, 2018."},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] P.O. Pinheiro and R. Collobert, \u201cFrom image-level to pixel-level labeling with convolutional networks,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1713-1721, 2015. 10.1109\/cvpr.2015.7298780","DOI":"10.1109\/CVPR.2015.7298780"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] S. Hong, J. Oh, H. Lee, and B. Han, \u201cLearning transferrable knowledge for semantic segmentation with deep convolutional neural network,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3204-3212, 2016. 10.1109\/cvpr.2016.349","DOI":"10.1109\/CVPR.2016.349"},{"key":"39","unstructured":"[39] S.J. Oh, R. Benenson, A. Khoreva, Z. Akata, M. Fritz, and B. Schiele, \u201cExploiting saliency for object segmentation from image level labels,\u201d Conference on Computer Vision and Pattern Recognition (CVPR), pp.5038-5047, 2017. 10.1109\/cvpr.2017.535"},{"key":"40","doi-asserted-by":"crossref","unstructured":"[40] P. Tokmakov, K. Alahari, and C. Schmid, \u201cWeakly-supervised semantic segmentation using motion cues,\u201d European Conference on Computer Vision, pp.388-404, 2016. 10.1007\/978-3-319-46493-0_24","DOI":"10.1007\/978-3-319-46493-0_24"},{"key":"41","doi-asserted-by":"crossref","unstructured":"[41] S. Hong, D. Yeo, S. Kwak, H. Lee, and B. Han, \u201cWeakly supervised semantic segmentation using web-crawled videos,\u201d IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2224-2232, 2017. 10.1109\/cvpr.2017.239","DOI":"10.1109\/CVPR.2017.239"},{"key":"42","doi-asserted-by":"crossref","unstructured":"[42] R. Fan, Q. Hou, M.M. Cheng, G. Yu, R.R. Martin, and S.M. Hu, \u201cAssociating inter-image salient instances for weakly supervised semantic segmentation,\u201d Proc. European Conference on Computer Vision (ECCV), pp.367-383, 2018. 10.1007\/978-3-030-01240-3_23","DOI":"10.1007\/978-3-030-01240-3_23"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] A. Roy and S. Todorovic, \u201cCombining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3529-3538, 2017. 10.1109\/cvpr.2017.770","DOI":"10.1109\/CVPR.2017.770"},{"key":"44","doi-asserted-by":"crossref","unstructured":"[44] D. Kim, D. Cho, D. Yoo, and I. So Kweon, \u201cTwo-phase learning for weakly supervised object localization,\u201d Proc. IEEE International Conference on Computer Vision, pp.3534-3543, 2017. 10.1109\/iccv.2017.382","DOI":"10.1109\/ICCV.2017.382"},{"key":"45","doi-asserted-by":"crossref","unstructured":"[45] A. Chaudhry, P.K. Dokania, and P.H. Torr, \u201cDiscovering class-specific pixels for weakly-supervised semantic segmentation,\u201d arXiv preprint arXiv:1707.05821, 2017. 10.48550\/arXiv.1707.05821","DOI":"10.5244\/C.31.20"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] X. Wang, S. You, X. Li, and H. 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