{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:02:48Z","timestamp":1777654968388,"version":"3.51.4"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729393","type":"print"},{"value":"9783031729409","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"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\/978-3-031-72940-9_8","type":"book-chapter","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T20:40:39Z","timestamp":1731789639000},"page":"126-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Strike a\u00a0Balance in\u00a0Continual Panoptic Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-4463","authenticated-orcid":false,"given":"Jinpeng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-4008","authenticated-orcid":false,"given":"Runmin","family":"Cong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1003-2252","authenticated-orcid":false,"given":"Yuxuan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1509-9002","authenticated-orcid":false,"given":"Horace Ho Shing","family":"Ip","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7484-7261","authenticated-orcid":false,"given":"Sam","family":"Kwong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,17]]},"reference":[{"key":"8_CR1","unstructured":"Baek, D., Oh, Y., Lee, S., Lee, J., Ham, B.: Decomposed knowledge distillation for class-incremental semantic segmentation. In: NeurIPS (2022)"},{"key":"8_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Cermelli, F., Cord, M., Douillard, A.: CoMFormer: continual learning in semantic and panoptic segmentation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00294"},{"key":"8_CR4","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: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00925"},{"key":"8_CR5","unstructured":"Cha, S., Yoo, Y., Moon, T., et\u00a0al.: SSUL: semantic segmentation with unknown label for exemplar-based class-incremental learning. In: NeurIPS (2021)"},{"key":"8_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1007\/978-3-030-01252-6_33","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Chaudhry","year":"2018","unstructured":"Chaudhry, A., Dokania, P.K., Ajanthan, T., Torr, P.H.S.: Riemannian walk for incremental learning: understanding forgetting and intransigence. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 556\u2013572. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_33"},{"issue":"6","key":"8_CR7","doi-asserted-by":"publisher","first-page":"3392","DOI":"10.1109\/TCYB.2023.3326165","volume":"54","author":"J Chen","year":"2024","unstructured":"Chen, J., Cong, R., Ip, H.H.S., Kwong, S.: Kepsalinst: using peripheral points to delineate salient instances. IEEE Trans. Cybern. 54(6), 3392\u20133405 (2024)","journal-title":"IEEE Trans. Cybern."},{"key":"8_CR8","unstructured":"Chen, J., Cong, R., Yuxuan, L., Ip, H., Kwong, S.: Saving 100x storage: prototype replay for reconstructing training sample distribution in class-incremental semantic segmentation. In: NeurIPS (2023)"},{"key":"8_CR9","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: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"8_CR10","unstructured":"Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. In: NeurIPS (2021)"},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"6501","DOI":"10.1109\/TMM.2024.3352921","volume":"26","author":"R Cong","year":"2024","unstructured":"Cong, R., Xiong, H., Chen, J., Zhang, W., Huang, Q., Zhao, Y.: Query-guided prototype evolution network for few-shot segmentation. IEEE Trans. Multimedia 26, 6501\u20136512 (2024)","journal-title":"IEEE Trans. Multimedia"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Dhar, P., Singh, R.V., Peng, K.C., Wu, Z., Chellappa, R.: Learning without memorizing. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00528"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Douillard, A., Chen, Y., Dapogny, A., Cord, M.: PLOP: Learning without forgetting for continual semantic segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00403"},{"key":"8_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58565-5_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Douillard","year":"2020","unstructured":"Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E.: PODNet: pooled outputs distillation for small-tasks incremental learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 86\u2013102. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_6"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Douillard, A., Ram\u00e9, A., Couairon, G., Cord, M.: DyTox: transformers for continual learning with dynamic token expansion. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00907"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Gu, Y., Deng, C., Wei, K.: Class-incremental instance segmentation via multi-teacher networks. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16238"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR19","unstructured":"Huang, Z., et al.: Learning prompt with distribution-based feature replay for few-shot class-incremental learning. arXiv preprint arXiv:2401.01598 (2024)"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., Doll\u00e1r, P.: Panoptic segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00963"},{"issue":"12","key":"8_CR21","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"8_CR23","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: ICLR (2018)"},{"key":"8_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1007\/978-3-030-01225-0_5","volume-title":"Computer Vision \u2013 ECCV 2018","author":"A Mallya","year":"2018","unstructured":"Mallya, A., Davis, D., Lazebnik, S.: Piggyback: adapting a single network to multiple tasks by learning to mask weights. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 72\u201388. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_5"},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: adding multiple tasks to a single network by iterative pruning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Maracani, A., Michieli, U., Toldo, M., Zanuttigh, P.: RECALL: Replay-based continual learning in semantic segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00694"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Michieli, U., Zanuttigh, P.: Incremental learning techniques for semantic segmentation. In: ICCVW (2019)","DOI":"10.1109\/ICCVW.2019.00400"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Michieli, U., Zanuttigh, P.: Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00117"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Ostapenko, O., Puscas, M., Klein, T., Jahnichen, P., Nabi, M.: Learning to remember: a synaptic plasticity driven framework for continual learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01158"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"8_CR31","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: NeurIPS (2017)"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Xiao, J.W., Zhang, C.B., Feng, J., Liu, X., van\u00a0de Weijer, J., Cheng, M.M.: Endpoints weight fusion for class incremental semantic segmentation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00696"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: DER: dynamically expandable representation for class incremental learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Yang, G., et al.: Uncertainty-aware contrastive distillation for incremental semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2567\u20132581 (2023)","DOI":"10.1109\/TPAMI.2022.3163806"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, C.B., Xiao, J.W., Liu, X., Chen, Y.C., Cheng, M.M.: Representation compensation networks for continual semantic segmentation. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00692"},{"key":"8_CR36","unstructured":"Zhang, Z., Gao, G., Fang, Z., Jiao, J., Wei, Y.: Mining unseen classes via regional objectness: a simple baseline for incremental segmentation. In: NeurIPS (2022)"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72940-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T21:32:33Z","timestamp":1731792753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72940-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,17]]},"ISBN":["9783031729393","9783031729409"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72940-9_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,17]]},"assertion":[{"value":"17 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}