{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:58:17Z","timestamp":1755694697948,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031783463"},{"type":"electronic","value":"9783031783470"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78347-0_10","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T14:54:07Z","timestamp":1733064847000},"page":"147-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Task Consistent Prototype Learning for\u00a0Incremental Few-Shot Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Wenbo","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yanan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Haoran","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Cermelli, F., Mancini, M., Bulo, S.R., Ricci, E., Caputo, B.: Modeling the background for incremental learning in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9233\u20139242 (2020)","DOI":"10.1109\/CVPR42600.2020.00925"},{"key":"10_CR2","unstructured":"Cermelli, F., Mancini, M., Xian, Y., Akata, Z., Caputo, B.: Prototype-based incremental few-shot semantic segmentation. arXiv preprint arXiv:2012.01415 (2020)"},{"key":"10_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"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\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chi, Z., Gu, L., Liu, H., Wang, Y., Yu, Y., Tang, J.: MetaFSCIL: a meta-learning approach for few-shot class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14166\u201314175 (2022)","DOI":"10.1109\/CVPR52688.2022.01377"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"key":"10_CR7","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126\u20131135. PMLR (2017)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4367\u20134375 (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Huang, K., Wang, F., Xi, Y., Gao, Y.: Prototypical kernel learning and open-set foreground perception for generalized few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19256\u201319265 (2023)","DOI":"10.1109\/ICCV51070.2023.01764"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., Doll\u00e1r, P.: Panoptic feature pyramid networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6399\u20136408 (2019)","DOI":"10.1109\/CVPR.2019.00656"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Lang, C., Cheng, G., Tu, B., Li, C., Han, J.: Base and meta: a new perspective on few-shot segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3265865"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J., Kim, J.: Adaptive prototype learning and allocation for few-shot segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8334\u20138343 (2021)","DOI":"10.1109\/CVPR46437.2021.00823"},{"key":"10_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"issue":"4","key":"10_CR15","first-page":"640","volume":"39","author":"J Long","year":"2015","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6941\u20136952 (2021)","DOI":"10.1109\/ICCV48922.2021.00686"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: Hierarchical dense correlation distillation for few-shot segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23641\u201323651 (2023)","DOI":"10.1109\/CVPR52729.2023.02264"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5822\u20135830 (2018)","DOI":"10.1109\/CVPR.2018.00610"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Shi, G., Wu, Y., Liu, J., Wan, S., Wang, W., Lu, T.: Incremental few-shot semantic segmentation via embedding adaptive-update and hyper-class representation. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5547\u20135556 (2022)","DOI":"10.1145\/3503161.3548218"},{"key":"10_CR20","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7262\u20137272 (2021)","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12183\u201312192 (2020)","DOI":"10.1109\/CVPR42600.2020.01220"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Tian, Z., et al.: Generalized few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11563\u201311572 (2022)","DOI":"10.1109\/CVPR52688.2022.01127"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chi, Z., Wang, Y., Feng, S.: MetaGCD: learning to continually learn in generalized category discovery. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1655\u20131665 (2023)","DOI":"10.1109\/ICCV51070.2023.00159"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Large scale incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 374\u2013382 (2019)","DOI":"10.1109\/CVPR.2019.00046"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Xian, Y., Choudhury, S., He, Y., Schiele, B., Akata, Z.: Semantic projection network for zero-and few-label semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8256\u20138265 (2019)","DOI":"10.1109\/CVPR.2019.00845"},{"key":"10_CR29","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Xu, W., Huang, H., Cheng, M., Yu, L., Wu, Q., Zhang, J.: Masked cross-image encoding for few-shot segmentation. In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp. 744\u2013749. IEEE (2023)","DOI":"10.1109\/ICME55011.2023.00133"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., Xu, Y.: Few-shot incremental learning with continually evolved classifiers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12455\u201312464 (2021)","DOI":"10.1109\/CVPR46437.2021.01227"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"10_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1007\/978-3-031-53305-1_19","volume-title":"MultiMedia Modeling","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., Chen, X., Guo, Y., Yu, J., Hong, R., Tian, Q.: Advancing incremental few-shot semantic segmentation via semantic-guided relation alignment and adaptation. In: Rudinac, S., et al. (eds.) MMM 2024. LNCS, vol. 14554, pp. 244\u2013257. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-53305-1_19"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, J., Yao, G., Zhou, W., Zhang, G., Ping, W., Zhang, W.: Feature distribution distillation-based few shot class incremental learning. In: 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI), pp. 108\u2013113. IEEE (2022)","DOI":"10.1109\/PRAI55851.2022.9904282"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Zhu, K., Cao, Y., Zhai, W., Cheng, J., Zha, Z.J.: Self-promoted prototype refinement for few-shot class-incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6801\u20136810 (2021)","DOI":"10.1109\/CVPR46437.2021.00673"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78347-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T15:02:39Z","timestamp":1733065359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78347-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031783463","9783031783470"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78347-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}