{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T08:16:28Z","timestamp":1759565788306,"version":"3.37.3"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["No. 2022ZD0118201"],"award-info":[{"award-number":["No. 2022ZD0118201"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 61972217","No. 32071459"],"award-info":[{"award-number":["No. 61972217","No. 32071459"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62176249","No. 62006133"],"award-info":[{"award-number":["No. 62176249","No. 62006133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62271465"],"award-info":[{"award-number":["No. 62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11263-024-02217-1","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T09:02:49Z","timestamp":1725267769000},"page":"1048-1066","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Adaptive Fuzzy Positive Learning for Annotation-Scarce Semantic Segmentation"],"prefix":"10.1007","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9292-2744","authenticated-orcid":false,"given":"Pengchong","family":"Qiao","sequence":"first","affiliation":[]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Baigui","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhennan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiawu","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"2217_CR1","doi-asserted-by":"publisher","unstructured":"Ahmed, W., Morerio, P., & Murino, V. (2022). Cleaning noisy labels by negative ensemble learning for source-free unsupervised domain adaptation. In IEEE\/CVF winter conference on applications of computer vision (pp 1616\u20131625). https:\/\/doi.org\/10.1109\/wacv51458.2022.00043","DOI":"10.1109\/wacv51458.2022.00043"},{"key":"2217_CR2","doi-asserted-by":"publisher","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N. E., & McGuinness, K. (2020). Pseudo-labeling and confirmation bias in deep semi-supervised learning. In International joint conference on neural networks (pp. 1\u20138), IEEE. https:\/\/doi.org\/10.1109\/ijcnn48605.2020.9207304","DOI":"10.1109\/ijcnn48605.2020.9207304"},{"key":"2217_CR3","doi-asserted-by":"publisher","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019). Semantickitti: A dataset for semantic scene understanding of lidar sequences. In Proceedings of the IEEE\/CVF international conference on computer vision (pp 9297\u20139307). https:\/\/doi.org\/10.1109\/iccv.2019.00939","DOI":"10.1109\/iccv.2019.00939"},{"key":"2217_CR4","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. A. (2019). Mixmatch: A holistic approach to semi-supervised learning. Advances in Neural Information Processing Systems 32"},{"key":"2217_CR5","unstructured":"Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020). Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33, 9912\u20139924."},{"key":"2217_CR6","doi-asserted-by":"publisher","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE\/CVF international conference on computer vision. https:\/\/doi.org\/10.1109\/iccv48922.2021.00951","DOI":"10.1109\/iccv48922.2021.00951"},{"key":"2217_CR7","doi-asserted-by":"publisher","unstructured":"Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834\u2013848. https:\/\/doi.org\/10.1109\/tpami.2017.2699184","DOI":"10.1109\/tpami.2017.2699184"},{"key":"2217_CR8","doi-asserted-by":"publisher","unstructured":"Cho, J. H., Mall, U., Bala, K., & Hariharan, B., (2021). Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 16794\u201316804). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01652","DOI":"10.1109\/cvpr46437.2021.01652"},{"key":"2217_CR9","doi-asserted-by":"publisher","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp 3213\u20133223). https:\/\/doi.org\/10.1109\/cvpr.2016.350","DOI":"10.1109\/cvpr.2016.350"},{"key":"2217_CR10","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s11263-019-01183-3","volume":"127","author":"I Croitoru","year":"2019","unstructured":"Croitoru, I., Bogolin, S. V., & Leordeanu, M. (2019). Unsupervised learning of foreground object segmentation. International Journal of Computer Vision, 127, 1279\u20131302. https:\/\/doi.org\/10.1007\/s11263-019-01183-3","journal-title":"International Journal of Computer Vision"},{"key":"2217_CR11","unstructured":"Dugas, C., Bengio, Y., B\u00e9lisle, F., Nadeau, C., & Garcia, R., (2001). Incorporating second-order functional knowledge for better option pricing. Advances in Neural Information Processing Systems, 472\u2013478"},{"key":"2217_CR12","doi-asserted-by":"publisher","unstructured":"Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88, 303\u2013338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4","DOI":"10.1007\/s11263-009-0275-4"},{"key":"2217_CR13","doi-asserted-by":"publisher","unstructured":"Fan, J., Gao, B., Jin, H., & Jiang, L. (2022). Ucc: Uncertainty guided cross-head co-training for semi-supervised semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 9947\u20139956). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00971","DOI":"10.1109\/cvpr52688.2022.00971"},{"key":"2217_CR14","doi-asserted-by":"publisher","unstructured":"Feng, Z., Zhou, Q., Gu, Q., Tan, X., Cheng, G., Lu, X., Shi, J., & Ma, L. (2022). Dmt: Dynamic mutual training for semi-supervised learning. Pattern Recognition, 108777. https:\/\/doi.org\/10.1016\/j.patcog.2022.108777","DOI":"10.1016\/j.patcog.2022.108777"},{"key":"2217_CR15","doi-asserted-by":"publisher","unstructured":"Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In International conference on artificial intelligence and statistics (pp 315\u2013323). https:\/\/doi.org\/10.1109\/icassp.2013.6639016","DOI":"10.1109\/icassp.2013.6639016"},{"key":"2217_CR16","doi-asserted-by":"publisher","unstructured":"Guo, X., Yang, C., Li, B., & Yuan, Y. (2021). Metacorrection: Domain-aware meta loss correction for unsupervised domain adaptation in semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 3927\u20133936). https:\/\/doi.org\/10.1109\/cvpr46437.2021.00392","DOI":"10.1109\/cvpr46437.2021.00392"},{"key":"2217_CR17","unstructured":"Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., & Freeman, W. T. (2021). Unsupervised semantic segmentation by distilling feature correspondences. In International conference on learning representations"},{"key":"2217_CR18","doi-asserted-by":"publisher","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Bourdev, L., Maji, S., & Malik, J. (2011). Semantic contours from inverse detectors. In: Proceedings of the IEEE\/CVF international conference on computer vision (pp 991\u2013998). IEEE. https:\/\/doi.org\/10.1109\/iccv.2011.6126343","DOI":"10.1109\/iccv.2011.6126343"},{"key":"2217_CR19","unstructured":"Huang, J., Guan, D., Xiao, A., & Lu, S. (2021). Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data. Advances in Neural Information Processing Systems, 34, 3635\u20133649."},{"key":"2217_CR20","doi-asserted-by":"publisher","unstructured":"Hwang, J. J., Yu, S. X., Shi, J., Collins, M. D., Yang, T. J., Zhang, X., & Chen, L. C. (2019). Segsort: Segmentation by discriminative sorting of segments. In Proceedings of the IEEE\/CVF international conference on computer vision (pp 7334\u20137344). https:\/\/doi.org\/10.1109\/iccv.2019.00743","DOI":"10.1109\/iccv.2019.00743"},{"key":"2217_CR21","doi-asserted-by":"publisher","unstructured":"Ji, X., Henriques, J. F., & Vedaldi, A. (2019). Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp 9865\u20139874). https:\/\/doi.org\/10.1109\/iccv.2019.00996","DOI":"10.1109\/iccv.2019.00996"},{"key":"2217_CR22","doi-asserted-by":"publisher","unstructured":"Ke, T. W., Hwang, J. J., Guo, Y., Wang, X., & Yu, S. X. (2022). Unsupervised hierarchical semantic segmentation with multiview cosegmentation and clustering transformers. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 2571\u20132581). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00260","DOI":"10.1109\/cvpr52688.2022.00260"},{"key":"2217_CR23","doi-asserted-by":"publisher","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.Y., Doll\u00e1r, P., & Girshick, R. (2023). Segment anything. In Proceedings of the IEEE\/CVF international conference on computer vision. https:\/\/doi.org\/10.1109\/ICCV51070.2023.00371","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"2217_CR24","doi-asserted-by":"publisher","unstructured":"Kundu, J. N., Kulkarni, A., Singh, A., Jampani, V., & Babu, R. V. (2021). Generalize then adapt: Source-free domain adaptive semantic segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp 7046\u20137056). https:\/\/doi.org\/10.1109\/iccv48922.2021.00696","DOI":"10.1109\/iccv48922.2021.00696"},{"key":"2217_CR25","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1016\/j.neunet.2023.02.009","volume":"161","author":"J Lee","year":"2023","unstructured":"Lee, J., & Lee, G. (2023). Feature alignment by uncertainty and self-training for source-free unsupervised domain adaptation. Neural Networks, 161, 682\u2013692. https:\/\/doi.org\/10.1016\/j.neunet.2023.02.009","journal-title":"Neural Networks"},{"key":"2217_CR26","unstructured":"Lee, J., Jung, D., Yim, J., & Yoon, S. (2022). Confidence score for source-free unsupervised domain adaptation. In International conference on machine learning (pp 12365\u201312377). PMLR"},{"key":"2217_CR27","doi-asserted-by":"publisher","unstructured":"Li, H., Wan, R., Wang, S., & Kot, A. C. (2021). Unsupervised domain adaptation in the wild via disentangling representation learning. International Journal of Computer Vision, 129, 267\u2013283. https:\/\/doi.org\/10.1007\/s11263-020-01364-5","DOI":"10.1007\/s11263-020-01364-5"},{"key":"2217_CR28","doi-asserted-by":"publisher","unstructured":"Li, K., Wang, Z., Cheng, Z., Yu, R., Zhao, Y., Song, G., Liu, C., Yuan, L., & Chen, J. (2023). Acseg: Adaptive conceptualization for unsupervised semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 7162\u20137172). https:\/\/doi.org\/10.1109\/cvpr52729.2023.00692","DOI":"10.1109\/cvpr52729.2023.00692"},{"key":"2217_CR29","doi-asserted-by":"publisher","unstructured":"Li, R., Li, S., He, C., Zhang, Y., Jia, X., & Zhang, L. (2022a). Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 11593\u201311603). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01130","DOI":"10.1109\/cvpr52688.2022.01130"},{"key":"2217_CR30","unstructured":"Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., & Duan, L. Y. (2022b). Uncertainty modeling for out-of-distribution generalization. International Conference on Learning Representations"},{"key":"2217_CR31","doi-asserted-by":"publisher","unstructured":"Li, Y. F., Zha, H. W., & Zhou, Z. H. (2017). Learning safe prediction for semi-supervised regression. In Proceedings of the AAAI conference on artificial intelligence. https:\/\/doi.org\/10.1609\/aaai.v31i1.10856","DOI":"10.1609\/aaai.v31i1.10856"},{"key":"2217_CR32","doi-asserted-by":"publisher","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017). Sphereface: Deep hypersphere embedding for face recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 212\u2013220). https:\/\/doi.org\/10.1109\/cvpr.2017.713","DOI":"10.1109\/cvpr.2017.713"},{"key":"2217_CR33","doi-asserted-by":"publisher","unstructured":"Liu, Y., Zhang, W., & Wang, J. (2021). Source-free domain adaptation for semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 1215\u20131224). https:\/\/doi.org\/10.1109\/cvpr46437.2021.00127","DOI":"10.1109\/cvpr46437.2021.00127"},{"key":"2217_CR34","doi-asserted-by":"publisher","unstructured":"Liu, Y., Tian, Y., Chen, Y., Liu, F., Belagiannis, V., & Carneiro, G. (2022). Perturbed and strict mean teachers for semi-supervised semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 4258\u20134267). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00422","DOI":"10.1109\/cvpr52688.2022.00422"},{"key":"2217_CR35","doi-asserted-by":"publisher","DOI":"10.1201\/9780429029608","author":"R McElreath","year":"2018","unstructured":"McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall\/CRC. https:\/\/doi.org\/10.1201\/9780429029608","journal-title":"Chapman and Hall\/CRC"},{"key":"2217_CR36","doi-asserted-by":"publisher","unstructured":"Mei, K., Zhu, C., Zou, J., & Zhang, S. (2020). Instance adaptive self-training for unsupervised domain adaptation. In European conference on computer vision (pp 415\u2013430). Springer. https:\/\/doi.org\/10.1007\/978-3-030-58574-7_25","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"2217_CR37","unstructured":"Melas-Kyriazi, L., Rupprecht, C., Laina, I., & Vedaldi, A. (2021). Finding an unsupervised image segmenter in each of your deep generative models. In International conference on learning representations"},{"key":"2217_CR38","doi-asserted-by":"publisher","unstructured":"Melas-Kyriazi, L., Rupprecht, C., Laina, I., & Vedaldi, A. (2022). Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 8364\u20138375). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00818","DOI":"10.1109\/cvpr52688.2022.00818"},{"key":"2217_CR39","doi-asserted-by":"publisher","unstructured":"Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. https:\/\/doi.org\/10.1109\/TPAMI.2021.3059968","DOI":"10.1109\/TPAMI.2021.3059968"},{"issue":"2","key":"2217_CR40","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.69.026113","volume":"69","author":"ME Newman","year":"2004","unstructured":"Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. https:\/\/doi.org\/10.1103\/physreve.69.026113","journal-title":"Physical Review E"},{"issue":"442","key":"2217_CR41","doi-asserted-by":"publisher","first-page":"287","DOI":"10.3390\/e18120442","volume":"18","author":"F Nielsen","year":"2017","unstructured":"Nielsen, F., & Sun, K. (2017). Guaranteed bounds on information-theoretic measures of univariate mixtures using piecewise log-sum-exp inequalities. Differential Geometrical Theory of Statistics, 18(442), 287. https:\/\/doi.org\/10.3390\/e18120442","journal-title":"Differential Geometrical Theory of Statistics"},{"key":"2217_CR42","unstructured":"Oliver, A., Odena, A., Raffel, C., Cubuk, E. D., & Goodfellow, I. J. (2018). Realistic evaluation of deep semi-supervised learning algorithms. In Advances in Neural Information Processing Systems (pp 3239\u20133250)"},{"key":"2217_CR43","doi-asserted-by":"publisher","unstructured":"Pan, F., Shin, I., Rameau, F., Lee, S., & Kweon, I. S. (2020). Unsupervised intra-domain adaptation for semantic segmentation through self-supervision. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 3764\u20133773). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00382","DOI":"10.1109\/cvpr42600.2020.00382"},{"issue":"1","key":"2217_CR44","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1023\/A:1013107507150","volume":"104","author":"JD Pint\u00e9r","year":"2001","unstructured":"Pint\u00e9r, J. D. (2001). Globally optimized spherical point arrangements: Model variants and illustrative results. Annals of Operations Research, 104(1), 213\u2013230. https:\/\/doi.org\/10.1023\/A:1013107507150","journal-title":"Annals of Operations Research"},{"key":"2217_CR45","doi-asserted-by":"publisher","unstructured":"Qiao, P., Wei, Z., Wang, Y., Wang, Z., Song, G., Xu, F., Ji, X., Liu, C., & Chen, J. (2023). Fuzzy positive learning for semi-supervised semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 15465\u201315474). https:\/\/doi.org\/10.1109\/cvpr52729.2023.01484","DOI":"10.1109\/cvpr52729.2023.01484"},{"key":"2217_CR46","doi-asserted-by":"publisher","unstructured":"Prabhu Teja, S., & Fleuret, F. (2021). Uncertainty reduction for model adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 9613\u20139623). https:\/\/doi.org\/10.1109\/cvpr46437.2021.00949","DOI":"10.1109\/cvpr46437.2021.00949"},{"key":"2217_CR47","doi-asserted-by":"publisher","unstructured":"Richter, S. R., Vineet, V., Roth, S., & Koltun, V. (2016). Playing for data: Ground truth from computer games. In European conference on computer vision (pp 102\u2013118), Springer. https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"2217_CR48","unstructured":"Rizve, M. N., Duarte, K., Rawat, Y. S., & Shah, M. (2020). In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning. In International conference on learning representations"},{"key":"2217_CR49","doi-asserted-by":"publisher","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 3234\u20133243). https:\/\/doi.org\/10.1109\/cvpr.2016.352","DOI":"10.1109\/cvpr.2016.352"},{"key":"2217_CR50","doi-asserted-by":"publisher","unstructured":"Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. https:\/\/doi.org\/10.21236\/ada164453","DOI":"10.21236\/ada164453"},{"key":"2217_CR51","unstructured":"Seitzer, M., Horn, M., Zadaianchuk, A., D. Zietlow, D., Xiao, T., Simon-Gabriel, C. J., He, T., Zhang, Z., Sch\u00f6lkopf, B., Brox, T., & Locatello, F. (2022). Bridging the gap to real-world object-centric learning. In International conference on learning representations"},{"key":"2217_CR52","doi-asserted-by":"publisher","unstructured":"Sim\u00e9oni, O., Sekkat, C., Puy, G., Vobeck\u00fd, A., Zablocki, \u00c9., & P\u2019erez, P. (2023). Unsupervised object localization: Observing the background to discover objects. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 3176\u20133186). https:\/\/doi.org\/10.1109\/cvpr52729.2023.00310","DOI":"10.1109\/cvpr52729.2023.00310"},{"key":"2217_CR53","unstructured":"Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C. A., Cubuk, E. D., Kurakin, A., & Li, C. L. (2020). Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in Neural Information Processing Systems 33"},{"key":"2217_CR54","doi-asserted-by":"publisher","unstructured":"Stan, S., & Rostami, M. (2021). Unsupervised model adaptation for continual semantic segmentation. In Proceedings of the AAAI conference on artificial intelligence (pp 2593\u20132601). https:\/\/doi.org\/10.1609\/aaai.v35i3.16362","DOI":"10.1609\/aaai.v35i3.16362"},{"key":"2217_CR55","doi-asserted-by":"publisher","unstructured":"Sun, Y., Cheng, C., Zhang, Y., Zhang, C., Zheng, L., Wang, Z., & Wei, Y. (2020). Circle loss: A unified perspective of pair similarity optimization. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 6398\u20136407). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00643","DOI":"10.1109\/cvpr42600.2020.00643"},{"key":"2217_CR56","doi-asserted-by":"publisher","unstructured":"Van\u00a0Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021). Unsupervised semantic segmentation by contrasting object mask proposals. In Proceedings of the IEEE\/CVF international conference on computer vision (pp 10052\u201310062). https:\/\/doi.org\/10.1109\/iccv48922.2021.00990","DOI":"10.1109\/iccv48922.2021.00990"},{"key":"2217_CR57","doi-asserted-by":"publisher","unstructured":"Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018). Cosface: Large margin cosine loss for deep face recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 5265\u20135274). https:\/\/doi.org\/10.1109\/cvpr.2018.00552","DOI":"10.1109\/cvpr.2018.00552"},{"key":"2217_CR58","doi-asserted-by":"publisher","unstructured":"Wang, X., Yu, Z., De\u00a0Mello, S., Kautz, J., Anandkumar, A., Shen, C., & Alvarez, J. M. (2022a). Freesolo: Learning to segment objects without annotations. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 14176\u201314186). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01378","DOI":"10.1109\/cvpr52688.2022.01378"},{"key":"2217_CR59","doi-asserted-by":"publisher","unstructured":"Wang, Y., Wang, H., Shen, Y., Fei, J., Li, W., Jin, G., Wu, L., Zhao, R., & Le, X. (2022b). Semi-supervised semantic segmentation using unreliable pseudo-labels. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 4248\u20134257). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00421","DOI":"10.1109\/cvpr52688.2022.00421"},{"key":"2217_CR60","unstructured":"Wen, X., Zhao, B., Zheng, A., Zhang, X., & Qi, X. (2022). Self-supervised visual representation learning with semantic grouping. Advances in Neural Information Processing Systems, 35, 16423\u201316438."},{"key":"2217_CR61","doi-asserted-by":"publisher","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., & Sun, J. (2018). Unified perceptual parsing for scene understanding. In European conference on computer vision (pp 418\u2013434). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_26","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"2217_CR62","doi-asserted-by":"publisher","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., & Gao, Y. (2022). St++: Make self-training work better for semi-supervised semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 4268\u20134277). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00423","DOI":"10.1109\/cvpr52688.2022.00423"},{"key":"2217_CR63","doi-asserted-by":"publisher","unstructured":"Ye, M., Zhang, J., Ouyang, J., & Yu, D. (2021). Source data-free unsupervised domain adaptation for semantic segmentation. In Proceedings of the 29th ACM international conference on multimedia (pp 2233\u20132242). https:\/\/doi.org\/10.1145\/3474085.3475384","DOI":"10.1145\/3474085.3475384"},{"key":"2217_CR64","doi-asserted-by":"publisher","unstructured":"Yin, Z., Wang, P., Wang, F., Xu, X., Zhang, H., Li, H., & Jin, R. (2022). Transfgu: a top-down approach to fine-grained unsupervised semantic segmentation. In European conference on computer vision (pp 73\u201389). Springer. https:\/\/doi.org\/10.1007\/978-3-031-19818-2_5","DOI":"10.1007\/978-3-031-19818-2_5"},{"key":"2217_CR65","doi-asserted-by":"publisher","unstructured":"You, F., Li, J., Zhu, L., Chen, Z., & Huang, Z. (2021). Domain adaptive semantic segmentation without source data. In Proceedings of the 29th ACM international conference on multimedia (pp 3293\u20133302). https:\/\/doi.org\/10.1145\/3474085.3475482","DOI":"10.1145\/3474085.3475482"},{"key":"2217_CR66","unstructured":"Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., & Brox, T. (2023). Unsupervised semantic segmentation with self-supervised object-centric representations. In International conference on learning representations"},{"key":"2217_CR67","doi-asserted-by":"publisher","unstructured":"Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., & Wen, F. (2021). Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 12414\u201312424). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01223","DOI":"10.1109\/cvpr46437.2021.01223"},{"key":"2217_CR68","doi-asserted-by":"publisher","unstructured":"Zhang, R., Isola, P., & Efros, A. A. (2016). Colorful image colorization. In European conference on computer vision (pp 649\u2013666). Springer. https:\/\/doi.org\/10.1007\/978-3-319-46487-9_40","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"2217_CR69","doi-asserted-by":"publisher","unstructured":"Zhao, D., Wang, S., Zang, Q., Quan, D., Ye, X., & Jiao, L. (2023). Towards better stability and adaptability: Improve online self-training for model adaptation in semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 11733\u201311743). https:\/\/doi.org\/10.1109\/cvpr52729.2023.01129","DOI":"10.1109\/cvpr52729.2023.01129"},{"key":"2217_CR70","doi-asserted-by":"publisher","unstructured":"Zheng, Z., & Yang, Y. (2021). Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. International Journal of Computer Vision, 129(4), 1106\u20131120. https:\/\/doi.org\/10.1007\/s11263-020-01395-y","DOI":"10.1007\/s11263-020-01395-y"},{"key":"2217_CR71","doi-asserted-by":"publisher","unstructured":"Ziegler, A., & Asano, Y. M. (2022). Self-supervised learning of object parts for semantic segmentation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp 14502\u201314511). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01410","DOI":"10.1109\/cvpr52688.2022.01410"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02217-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-024-02217-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-024-02217-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T10:03:09Z","timestamp":1740391389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-024-02217-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,2]]},"references-count":71,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["2217"],"URL":"https:\/\/doi.org\/10.1007\/s11263-024-02217-1","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"type":"print","value":"0920-5691"},{"type":"electronic","value":"1573-1405"}],"subject":[],"published":{"date-parts":[[2024,9,2]]},"assertion":[{"value":"13 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}