{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:22:33Z","timestamp":1783520553235,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819533923","type":"print"},{"value":"9789819533930","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3393-0_26","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T09:51:00Z","timestamp":1761904260000},"page":"316-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Video Domain Incremental Learning for\u00a0Human Action Recognition in\u00a0Home Environments"],"prefix":"10.1007","author":[{"given":"Yuanda","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiani","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","unstructured":"Alssum, L., Le\u00f3n\u00a0Alc\u00e1zar, J., Ramazanova, M., Zhao, C., Ghanem, B.: Just a glimpse: rethinking temporal information for video continual learning. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada, pp. 2474\u20132483. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPRW59228.2023.00246","DOI":"10.1109\/CVPRW59228.2023.00246"},{"key":"26_CR2","unstructured":"Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 15920\u201315930. Curran Associates, Inc. (2020)"},{"key":"26_CR3","doi-asserted-by":"publisher","unstructured":"Das, S., et al.: Toyota smarthome: real-world activities of daily living. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27\u2013November 2 2019, pp. 833\u2013842. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00092","DOI":"10.1109\/ICCV.2019.00092"},{"key":"26_CR4","doi-asserted-by":"publisher","unstructured":"Delange, M., et al.: A continual learning survey: defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell.,\u00a01 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2021.3057446","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"26_CR5","doi-asserted-by":"publisher","unstructured":"Jang, J., Kim, D., Park, C., Jang, M., Lee, J., Kim, J.: ETRI-activity3D: a large-scale RGB-D dataset for robots to recognize daily activities of the elderly. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, Las Vegas, NV, USA, 24 October 2020\u201324 January 2021, pp. 10990\u201310997. IEEE (2020). https:\/\/doi.org\/10.1109\/IROS45743.2020.9341160","DOI":"10.1109\/IROS45743.2020.9341160"},{"issue":"13","key":"26_CR6","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":"26_CR7","doi-asserted-by":"publisher","unstructured":"Kong, Y., Fu, Y.: Human action recognition and prediction: a survey. Int. J. Comput. Vis. 130(5), 1366\u20131401 (2022). https:\/\/doi.org\/10.1007\/S11263-022-01594-9","DOI":"10.1007\/S11263-022-01594-9"},{"key":"26_CR8","doi-asserted-by":"publisher","unstructured":"Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., Rodr\u00edguez, N.D.: Continual learning for robotics: definition, framework, learning strategies, opportunities and challenges. Inf. Fusion 58, 52\u201368 (2020). https:\/\/doi.org\/10.1016\/J.INFFUS.2019.12.004","DOI":"10.1016\/J.INFFUS.2019.12.004"},{"issue":"12","key":"26_CR9","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2018","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935\u20132947 (2018). https:\/\/doi.org\/10.1109\/TPAMI.2017.2773081","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: adding multiple tasks to a single network by iterative pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7765\u20137773 (2018)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"26_CR11","doi-asserted-by":"publisher","unstructured":"Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A.D., Van De\u00a0Weijer, J.: Class-incremental learning: survey and performance evaluation on image classification. IEEE Trans. Pattern Anal. Mach. Intell., 1\u201320 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3213473","DOI":"10.1109\/TPAMI.2022.3213473"},{"key":"26_CR12","doi-asserted-by":"publisher","unstructured":"Park, J., Kang, M., Han, B.: Class-incremental learning for action recognition in videos. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, pp. 13678\u201313687. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.01344","DOI":"10.1109\/ICCV48922.2021.01344"},{"key":"26_CR13","doi-asserted-by":"publisher","unstructured":"Pei, Y., et al.: Space-time prompting for video class-incremental learning. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, pp. 11898\u201311908. IEEE (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01096","DOI":"10.1109\/ICCV51070.2023.01096"},{"key":"26_CR14","doi-asserted-by":"publisher","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 5533\u20135542. IEEE (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.587","DOI":"10.1109\/CVPR.2017.587"},{"key":"26_CR15","doi-asserted-by":"publisher","unstructured":"Royer, A., Lampert, C.H.: Classifier adaptation at prediction time. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7\u201312 June 2015, pp. 1401\u20131409. IEEE Computer Society (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298746","DOI":"10.1109\/CVPR.2015.7298746"},{"key":"26_CR16","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. CoRR abs\/1609.04747 (2016). http:\/\/arxiv.org\/abs\/1609.04747"},{"key":"26_CR17","doi-asserted-by":"publisher","unstructured":"Shahroudy, A., Liu, J., Ng, T., Wang, G.: NTU RGB+D: a large scale dataset for 3d human activity analysis. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 1010\u20131019. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.115","DOI":"10.1109\/CVPR.2016.115"},{"issue":"2","key":"26_CR18","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1080\/02664768700000020","volume":"14","author":"MC Shewry","year":"1987","unstructured":"Shewry, M.C., Wynn, H.P.: Maximum entropy sampling. J. Appl. Stat. 14(2), 165\u2013170 (1987)","journal-title":"J. Appl. Stat."},{"key":"26_CR19","doi-asserted-by":"publisher","unstructured":"Song, X., et al.: Spatio-temporal contrastive domain adaptation for action recognition. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 9782\u20139790. IEEE (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00966","DOI":"10.1109\/CVPR46437.2021.00966"},{"key":"26_CR20","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. CoRR abs\/1212.0402 (2012). http:\/\/arxiv.org\/abs\/1212.0402"},{"issue":"12","key":"26_CR21","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1038\/s42256-022-00568-3","volume":"4","author":"GM Ven","year":"2022","unstructured":"Ven, G.M., Tuytelaars, T., Tolias, A.S.: Three types of incremental learning. Nature Mach. Intell. 4(12), 1185\u20131197 (2022). https:\/\/doi.org\/10.1038\/s42256-022-00568-3","journal-title":"Nature Mach. Intell."},{"key":"26_CR22","doi-asserted-by":"publisher","unstructured":"Villa, A., et al.: PIVOT: prompting for video continual learning. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, pp. 24214\u201324223. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.02319","DOI":"10.1109\/CVPR52729.2023.02319"},{"key":"26_CR23","doi-asserted-by":"publisher","unstructured":"Villa, A., Alhamoud, K., Escorcia, V., Heilbron, F.C., Alcazar, J.L., Ghanem, B.: vCLIMB: a novel video class incremental learning benchmark. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 19013\u201319022. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01845","DOI":"10.1109\/CVPR52688.2022.01845"},{"key":"26_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-46484-8_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"L Wang","year":"2016","unstructured":"Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VIII. LNCS, vol. 9912, pp. 20\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_2"},{"key":"26_CR25","doi-asserted-by":"publisher","unstructured":"Wang, X., Xu, Y., Yang, J., Wen, B., Kot, A.C.: Confidence attention and generalization enhanced distillation for continuous video domain adaptation (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.10452","DOI":"10.48550\/arXiv.2303.10452"},{"key":"26_CR26","doi-asserted-by":"publisher","unstructured":"Wu, Y., et al.: Large scale incremental learning. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 374\u2013382. IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00046","DOI":"10.1109\/CVPR.2019.00046"},{"key":"26_CR27","doi-asserted-by":"publisher","unstructured":"Xu, Y., Cao, H., Mao, K., Chen, Z., Xie, L., Yang, J.: Aligning correlation information for domain adaptation in action recognition. IEEE Trans. Neural Networks Learn. Syst. 35(5), 6767\u20136778 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2022.3212909","DOI":"10.1109\/TNNLS.2022.3212909"},{"key":"26_CR28","doi-asserted-by":"publisher","unstructured":"Yang, L., Huang, Y., Sugano, Y., Sato, Y.: Interact before align: leveraging cross-modal knowledge for domain adaptive action recognition. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 14702\u201314712. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01431","DOI":"10.1109\/CVPR52688.2022.01431"},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, B., Xiao, X., Gan, G., Zhang, B., Xia, S.T.: Maintaining discrimination and fairness in class incremental learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13208\u201313217 (2020)","DOI":"10.1109\/CVPR42600.2020.01322"},{"key":"26_CR30","doi-asserted-by":"publisher","unstructured":"Zhao, H., Qin, X., Su, S., Fu, Y., Lin, Z., Li, X.: When video classification meets incremental classes. In: Proceedings of the 29th ACM International Conference on Multimedia, Virtual Event China, pp. 880\u2013889. ACM (2021). https:\/\/doi.org\/10.1145\/3474085.3475265","DOI":"10.1145\/3474085.3475265"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3393-0_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T16:29:47Z","timestamp":1775320187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3393-0_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9789819533923","9789819533930"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3393-0_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xuzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icig.csig.org.cn\/2025\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}