{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:16:46Z","timestamp":1758673006965,"version":"3.44.0"},"publisher-location":"Cham","reference-count":84,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031851803"},{"type":"electronic","value":"9783031851810"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-85181-0_1","type":"book-chapter","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T06:59:31Z","timestamp":1745305171000},"page":"3-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PARMESAN: Parameter-Free Memory Search and\u00a0Transduction for\u00a0Dense Prediction Tasks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2676-2096","authenticated-orcid":false,"given":"Philip Matthias","family":"Winter","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2599-2395","authenticated-orcid":false,"given":"Maria","family":"Wimmer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2300-2661","authenticated-orcid":false,"given":"Astrid","family":"Berg","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-3684","authenticated-orcid":false,"given":"David","family":"Major","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1563-7683","authenticated-orcid":false,"given":"Dimitrios","family":"Lenis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5926-9317","authenticated-orcid":false,"given":"Theresa","family":"Neubauer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8943-8813","authenticated-orcid":false,"given":"Gaia Romana","family":"De Paolis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6442-6172","authenticated-orcid":false,"given":"Johannes","family":"Novotny","sequence":"additional","affiliation":[]},{"given":"Sophia","family":"Ulonska","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0362-7998","authenticated-orcid":false,"given":"Katja","family":"B\u00fchler","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,23]]},"reference":[{"key":"1_CR1","unstructured":"Beck, M., et al.: xLSTM: Extended Long Short-Term Memory. arXiv preprint arXiv:2405.04517 (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.04517"},{"key":"1_CR2","unstructured":"Belhasin, O., Bar-Shalom, G., El-Yaniv, R.: TransBoost: Improving the best ImageNet performance using deep transduction. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) NeurIPS, vol.\u00a035, pp. 28363\u201328373 (2022). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/b60161e93f3e0e4207081a3b4ef5e8d8-Paper-Conference.pdf"},{"issue":"12","key":"1_CR3","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1038\/nn.4401","volume":"19","author":"MK Benna","year":"2016","unstructured":"Benna, M.K., Fusi, S.: Computational principles of synaptic memory consolidation. Nat. Neurosci. 19(12), 1697\u20131706 (2016). https:\/\/doi.org\/10.1038\/nn.4401","journal-title":"Nat. Neurosci."},{"key":"1_CR4","unstructured":"Bommasani, R., et\u00a0al.: On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). https:\/\/crfm.stanford.edu\/assets\/report.pdf"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Cermelli, F., Mancini, M., Bul\u00f2, S.R., Ricci, E., Caputo, B.: Modeling the background for incremental learning in semantic segmentation. In: CVPR, pp. 9230\u20139239 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00925","DOI":"10.1109\/CVPR42600.2020.00925"},{"key":"1_CR6","unstructured":"Chaudhry, A., et al.: Continual learning with tiny episodic memories. In: Workshop on Multi-Task and Lifelong Reinforcement Learning (2019)"},{"key":"1_CR7","unstructured":"Chrysakis, A., Moens, M.F.: Online continual learning from imbalanced data. In: International Conference on Machine Learning (2020). https:\/\/api.semanticscholar.org\/CorpusID:221082215"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"issue":"07","key":"1_CR9","doi-asserted-by":"publisher","first-page":"3366","DOI":"10.1109\/TPAMI.2021.3057446","volume":"44","author":"M De Lange","year":"2022","unstructured":"De Lange, M., et al.: A continual learning survey: Defying forgetting in classification tasks. IEEE TPAMI 44(07), 3366\u20133385 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3057446","journal-title":"IEEE TPAMI"},{"key":"1_CR10","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) NeurIPS, vol.\u00a029, pp. 3837\u20133845 (2016). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2016\/file\/04df4d434d481c5bb723be1b6df1ee65-Paper.pdf"},{"key":"1_CR11","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021). https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Edstedt, J., Sun, Q., B\u00f6kman, G., Wadenb\u00e4ck, M., Felsberg, M.: RoMa: Revisiting robust losses for dense feature matching. arXiv preprint arXiv:2305.15404 (2023). https:\/\/doi.org\/10.48550\/ARXIV.2305.15404","DOI":"10.48550\/ARXIV.2305.15404"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Fix, E., Hodges, J.L.: Discriminatory analysis. nonparametric discrimination: Consistency properties. Tech. Rep. Number 4, USAF School of Aviation Medicine, Randolph Field (1951)","DOI":"10.1037\/e471672008-001"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: 22nd ACM SIGSAC Conference on Computer and Communications Security, CCS \u201915, pp. 1322\u20131333 (2015). https:\/\/doi.org\/10.1145\/2810103.2813677","DOI":"10.1145\/2810103.2813677"},{"key":"1_CR15","unstructured":"French, R.M.: Catastrophic interference in connectionist networks: Can it be predicted, can it be prevented? In: NeurIPS, pp. 1176\u20131177 (1993)"},{"issue":"4","key":"1_CR16","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3(4), 128\u2013135 (1999). https:\/\/doi.org\/10.1016\/S1364-6613(99)01294-2","journal-title":"Trends Cogn. Sci."},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Galesso, S., Argus, M., Brox, T.: Far away in the deep space: Dense nearest-neighbor-based out-of-distribution detection. In: ICCVW (2023). http:\/\/lmb.informatik.uni-freiburg.de\/Publications\/2023\/GAB23","DOI":"10.1109\/ICCVW60793.2023.00482"},{"key":"1_CR18","unstructured":"Gammerman, A., Vovk, V., Vapnik, V.: Learning by transduction. In: 14th Conference on Uncertainty in Artificial Intelligence. pp. 148\u2013155. UAI\u201998 (1998)"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Gelbard-Sagiv, H., Mukamel, R., Harel, M., Malach, R., Fried, I.: Internally generated reactivation of single neurons in human hippocampus during free recall. Science 322(5898), 96\u2013101 (2008). http:\/\/www.jstor.org\/stable\/20144953","DOI":"10.1126\/science.1164685"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Girshick, R., Iandola, F., Darrell, T., Malik, J.: Deformable part models are convolutional neural networks. In: CVPR, pp. 437\u2013446 (2015)","DOI":"10.1109\/CVPR.2015.7298641"},{"key":"1_CR21","unstructured":"Graves, A., Wayne, G., Danihelka, I.: Neural turing machines. CoRR abs\/1410.5401 (2014). http:\/\/arxiv.org\/abs\/1410.5401"},{"issue":"7626","key":"1_CR22","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1038\/nature20101","volume":"538","author":"A Graves","year":"2016","unstructured":"Graves, A., et al.: Hybrid computing using a neural network with dynamic external memory. Nature 538(7626), 471\u2013476 (2016). https:\/\/doi.org\/10.1038\/nature20101","journal-title":"Nature"},{"key":"1_CR23","doi-asserted-by":"publisher","unstructured":"Grossberg, S.: How does a brain build a cognitive code? Psychol. Rev. 87, 1\u201351 (1980). https:\/\/doi.org\/10.1037\/0033-295X.87.1.1","DOI":"10.1037\/0033-295X.87.1.1"},{"key":"1_CR24","unstructured":"Guo, Z., Wang, K., Cazenavette, G., Li, H., Zhang, K., You, Y.: Towards lossless dataset distillation via difficulty-aligned trajectory matching. CoRR abs\/2310.05773 (2023)"},{"key":"1_CR25","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Hong, S., Kim, S.: Deep matching prior: Test-time optimization for dense correspondence. In: ICCV, pp. 9907\u20139917, October 2021","DOI":"10.1109\/ICCV48922.2021.00976"},{"key":"1_CR27","doi-asserted-by":"publisher","unstructured":"Islam, A., Lundell, B., Sawhney, H., Sinha, S.N., Morales, P., Radke, R.J.: Self-supervised learning with local contrastive loss for detection and semantic segmentation. In: IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5613\u20135622 (2023). https:\/\/doi.org\/10.1109\/WACV56688.2023.00558","DOI":"10.1109\/WACV56688.2023.00558"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., Yi, K.M.: COTR: Correspondence Transformer for Matching Across Images. In: ICCV, pp. 6207\u20136217 (2021)","DOI":"10.1109\/ICCV48922.2021.00615"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Yang, M., Tsirlin, M., Tang, R., Dai, Y., Lin, J.: \u201cLow-resource\u201d text classification: A parameter-free classification method with compressors. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 6810\u20136828 (2023). https:\/\/aclanthology.org\/2023.findings-acl.426","DOI":"10.18653\/v1\/2023.findings-acl.426"},{"key":"1_CR30","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"issue":"13","key":"1_CR31","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"J Kirkpatrick","year":"2017","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. Proc. Natl. Acad. Sci. 114(13), 3521\u20133526 (2017). https:\/\/doi.org\/10.1073\/pnas.1611835114","journal-title":"Proc. Natl. Acad. Sci."},{"key":"1_CR32","unstructured":"Knoblauch, J., Husain, H., Diethe, T.: Optimal continual learning has perfect memory and is NP-HARD. In: ICML, ICML 2020, pp. 5327\u20135337 (2020)"},{"key":"1_CR33","doi-asserted-by":"publisher","unstructured":"Krutsylo, A., Morawiecki, P.: Diverse memory for experience replay in continual learning. In: 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 91\u201396 (2022). https:\/\/doi.org\/10.14428\/ESANN\/2022.ES2022-83","DOI":"10.14428\/ESANN\/2022.ES2022-83"},{"key":"1_CR34","unstructured":"Lange, M.D., van\u00a0de Ven, G.M., Tuytelaars, T.: Continual evaluation for lifelong learning: Identifying the stability gap. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=Zy350cRstc6"},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Lee, K.Y., Zhong, Y., Wang, Y.X.: Do pre-trained models benefit equally in continual learning? 2023 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6474\u20136482 (2022). https:\/\/api.semanticscholar.org\/CorpusID:253223819","DOI":"10.1109\/WACV56688.2023.00642"},{"key":"1_CR36","doi-asserted-by":"publisher","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV, pp. 614\u2013629 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_37","DOI":"10.1007\/978-3-319-46493-0_37"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1_CR38","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, L., Yamada, M., Yang, Y.: Semantic correspondence as an optimal transport problem. In: CVPR, pp. 4463\u20134472 (2020)","DOI":"10.1109\/CVPR42600.2020.00452"},{"key":"1_CR39","unstructured":"Liu, Y., Zhu, M., Li, H., Chen, H., Wang, X., Shen, C.: Matcher: segment anything with one shot using all-purpose feature matching. arXiv preprint (2023)"},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A ConvNet for the 2020s. In: CVPR, pp. 11976\u201311983 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1_CR41","unstructured":"Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In: NeurIPS, pp. 6470\u20136479 (2017)"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Lyu, W., Chen, L., Zhou, Z., Wu, W.: Deep semantic feature matching using confidential correspondence consistency. IEEE Access 8, 12802\u201312814 (2020). https:\/\/api.semanticscholar.org\/CorpusID:210930361","DOI":"10.1109\/ACCESS.2020.2966655"},{"key":"1_CR43","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: Adding multiple tasks to a single network by iterative pruning. In: CVPR, pp. 7765\u20137773 (2018)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Mariotti, O., Mac\u00a0Aodha, O., Bilen, H.: Improving semantic correspondence with viewpoint-guided spherical maps. In: CVPR, pp. 19521\u201319530, June 2024","DOI":"10.1109\/CVPR52733.2024.01846"},{"key":"1_CR45","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1037\/0033-295X.102.3.419","volume":"102","author":"J McClelland","year":"1995","unstructured":"McClelland, J., Mcnaughton, B., O\u2019Reilly, R.: Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419\u201357 (1995). https:\/\/doi.org\/10.1037\/0033-295X.102.3.419","journal-title":"Psychol. Rev."},{"key":"1_CR46","doi-asserted-by":"publisher","unstructured":"McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. In: Bower, G.H. (ed.) Advances in Research and Theory, Psychology of Learning and Motivation, vol.\u00a024, pp. 109\u2013165 (1989). https:\/\/doi.org\/10.1016\/S0079-7421(08)60536-8","DOI":"10.1016\/S0079-7421(08)60536-8"},{"key":"1_CR47","doi-asserted-by":"crossref","unstructured":"Nakata, K., Ng, Y., Miyashita, D., Maki, A., Lin, Y.C., Deguchi, J.: Revisiting a kNN-based image classification system with high-capacity storage. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV, pp. 457\u2013474 (2022)","DOI":"10.1007\/978-3-031-19836-6_26"},{"key":"1_CR48","unstructured":"Paischer, F., Adler, T., Hofmarcher, M., Hochreiter, S.: Semantic helm: A human-readable memory for reinforcement learning (2023)"},{"key":"1_CR49","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"GI Parisi","year":"2019","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54\u201371 (2019). https:\/\/doi.org\/10.1016\/j.neunet.2019.01.012","journal-title":"Neural Netw."},{"key":"1_CR50","doi-asserted-by":"crossref","unstructured":"Prabhu, A., Torr, P., Dokania, P.: Gdumb: a simple approach that questions our progress in continual learning. In: ECCV, pp. 524\u2013540 (2020)","DOI":"10.1007\/978-3-030-58536-5_31"},{"key":"1_CR51","doi-asserted-by":"crossref","unstructured":"Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D. (eds.): Dataset Shift in Machine Learning. MIT Press (2009)","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"1_CR52","unstructured":"Ramsauer, H., et al.: Hopfield networks is all you need. In: ICLR (2021). https:\/\/openreview.net\/forum?id=tL89RnzIiCd"},{"key":"1_CR53","doi-asserted-by":"publisher","unstructured":"Rebuffi, S., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: CVPR, pp. 5533\u20135542 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.587","DOI":"10.1109\/CVPR.2017.587"},{"key":"1_CR54","doi-asserted-by":"publisher","unstructured":"Reiser, P., et al.: Graph neural networks for materials science and chemistry. Commun. Mater. 3, 93 (2022). https:\/\/doi.org\/10.1038\/s43246-022-00315-6","DOI":"10.1038\/s43246-022-00315-6"},{"key":"1_CR55","doi-asserted-by":"publisher","unstructured":"Ring, M.B.: Child: A first step towards continual learning. In: Thrun, S., Pratt, L. (eds.) Learning to Learn, pp. 261\u2013292 (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_11","DOI":"10.1007\/978-1-4615-5529-2_11"},{"key":"1_CR56","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovic, R., Sivic, J.: Convolutional neural network architecture for geometric matching. In: CVPR, pp. 39\u201348 (2017)","DOI":"10.1109\/CVPR.2017.12"},{"key":"1_CR57","unstructured":"Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., Wayne, G.: Experience replay for continual learning. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) NeurIPS, vol.\u00a032, pp. 348\u2013358 (2019). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/fa7cdfad1a5aaf8370ebeda47a1ff1c3-Paper.pdf"},{"key":"1_CR58","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention (MICCAI), vol.\u00a09351, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1_CR59","unstructured":"Rusu, A.A., et al.: Progressive neural networks. CoRR abs\/1606.04671 (2016)"},{"key":"1_CR60","doi-asserted-by":"crossref","unstructured":"Woo, S., et al.: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. arXiv preprint arXiv:2301.00808 (2023)","DOI":"10.1109\/CVPR52729.2023.01548"},{"issue":"1","key":"1_CR61","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2009). https:\/\/doi.org\/10.1109\/TNN.2008.2005605","journal-title":"IEEE Trans. Neural Networks"},{"key":"1_CR62","unstructured":"Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: NeurIPS, pp. 2994\u20143003 (2017)"},{"key":"1_CR63","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"J Shiraishi","year":"2000","unstructured":"Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. Am. J. Roentgenol. 174, 71\u201374 (2000)","journal-title":"Am. J. Roentgenol."},{"issue":"2","key":"1_CR64","doi-asserted-by":"publisher","first-page":"260","DOI":"10.3390\/electronics12020260","volume":"12","author":"HS Sikandar","year":"2023","unstructured":"Sikandar, H.S., Waheed, H., Tahir, S., Malik, S.U.R., Rafique, W.: A detailed survey on federated learning attacks and defenses. Electronics 12(2), 260 (2023). https:\/\/doi.org\/10.3390\/electronics12020260","journal-title":"Electronics"},{"key":"1_CR65","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: LoFTR: detector-free local feature matching with transformers. In: CVPR, pp. 8922\u20138931 (2021)","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"1_CR66","doi-asserted-by":"crossref","unstructured":"Sun, T., et al.: SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In: CVPR, pp. 21371\u201321382, June 2022","DOI":"10.1109\/CVPR52688.2022.02068"},{"key":"1_CR67","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: Self-supervised pre-training of swin transformers for 3d medical image analysis. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20698\u201320708 (2021). https:\/\/api.semanticscholar.org\/CorpusID:244715046","DOI":"10.1109\/CVPR52688.2022.02007"},{"key":"1_CR68","doi-asserted-by":"publisher","unstructured":"Thrun, S.: Lifelong learning algorithms. In: Thrun, S., Pratt, L. (eds.) Learning to Learn, pp. 181\u2013209 (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2_8","DOI":"10.1007\/978-1-4615-5529-2_8"},{"key":"1_CR69","doi-asserted-by":"crossref","unstructured":"Thrun, S., Mitchell, T.M.: Lifelong robot learning. In: Steels, L. (ed.) The Biology and Technology of Intelligent Autonomous Agents, pp. 165\u2013196 (1995)","DOI":"10.1007\/978-3-642-79629-6_7"},{"key":"1_CR70","unstructured":"Truong, P., Danelljan, M., Gool, L.V., Timofte, R.: GOCor: bringing globally optimized correspondence volumes into your neural network. In: NeurIPS, pp. 14278\u201314290 (2020)"},{"key":"1_CR71","doi-asserted-by":"publisher","unstructured":"Ufer, N., Ommer, B.: Deep semantic feature matching. In: CVPR, pp. 5929\u20135938 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.628","DOI":"10.1109\/CVPR.2017.628"},{"key":"1_CR72","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) NeurIPS, vol.\u00a030, pp. 5998\u20136008 (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"1_CR73","unstructured":"van\u00a0de Ven, G.M., Tolias, A.S.: Three scenarios for continual learning. CoRR abs\/1904.07734 (2019). http:\/\/arxiv.org\/abs\/1904.07734"},{"key":"1_CR74","doi-asserted-by":"crossref","unstructured":"Verwimp, E., De\u00a0Lange, M., Tuytelaars, T.: Rehearsal revealed: The limits and merits of revisiting samples in continual learning. In: ICCV, pp. 9385\u20139394 (2021)","DOI":"10.1109\/ICCV48922.2021.00925"},{"key":"1_CR75","doi-asserted-by":"publisher","unstructured":"Wang, L., Zhang, X., Su, H., Zhu, J.: A comprehensive survey of continual learning: theory, method and application. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201320 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2024.3367329","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"1_CR76","unstructured":"Wang, T., Zhu, J., Torralba, A., Efros, A.A.: Dataset distillation. CoRR abs\/1811.10959 (2018). http:\/\/arxiv.org\/abs\/1811.10959"},{"key":"1_CR77","unstructured":"Wang, X.: Learning and Reasoning with Visual Correspondence in Time. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA, September 2019"},{"key":"1_CR78","unstructured":"Weston, J., Chopra, S., Bordes, A.: Memory networks. In: ICLR (2015)"},{"issue":"2","key":"1_CR79","doi-asserted-by":"publisher","first-page":"2567","DOI":"10.1109\/TPAMI.2022.3163806","volume":"45","author":"G Yang","year":"2023","unstructured":"Yang, G., Fini, E., Xu, D., Rota, P., Ding, M., Nabi, M., Alameda-Pineda, X., Ricci, E.: Uncertainty-aware contrastive distillation for incremental semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 2567\u20132581 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3163806","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1_CR80","unstructured":"Yuan, B., Zhao, D.: A survey on continual semantic segmentation: Theory, challenge, method and application (2023)"},{"key":"1_CR81","doi-asserted-by":"publisher","unstructured":"Zhang, X.M., Liang, L., Liu, L., Tang, M.J.: Graph neural networks and their current applications in bioinformatics. Front. Genetics 12, 690049 (2021). https:\/\/doi.org\/10.3389\/fgene.2021.690049","DOI":"10.3389\/fgene.2021.690049"},{"key":"1_CR82","doi-asserted-by":"crossref","unstructured":"Zhao, D., Song, Z., Ji, Z., Zhao, G., Ge, W., Yu, Y.: Multi-scale matching networks for semantic correspondence. In: ICCV, pp. 3354\u20133364 (2021)","DOI":"10.1109\/ICCV48922.2021.00334"},{"key":"1_CR83","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: CVPR, pp. 5122\u20135130, July 2017","DOI":"10.1109\/CVPR.2017.544"},{"key":"1_CR84","unstructured":"Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: NeurIPS, pp. 321\u2013328 (2003)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-85181-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T13:11:07Z","timestamp":1758633067000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-85181-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031851803","9783031851810"],"references-count":84,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-85181-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}