{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:17:37Z","timestamp":1743146257902,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031023743"},{"type":"electronic","value":"9783031023750"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-02375-0_10","type":"book-chapter","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:03:10Z","timestamp":1652180590000},"page":"130-143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SARNN: A Spatiotemporal Prediction Model for Reducing Error Transmissions"],"prefix":"10.1007","author":[{"given":"Yonghui","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingqi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremy Jianshuo-li","family":"Mahr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Ushiku, Y.: Long short-term memory, In: Neural Computation, pp. 1\u20137 (1997)","DOI":"10.1007\/978-3-030-03243-2_856-1"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Klein, B., Wolf, L., Afek, Y.: A dynamic convolutional layer for short range weather prediction. In: Computer Vision and Pattern Recognition, pp. 4840\u20134848 (2015)","DOI":"10.1109\/CVPR.2015.7299117"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: International Joint Conference on Artificial Intelligence, pp. 3634\u20133640 (2018)","DOI":"10.24963\/ijcai.2018\/505"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Liang, X., Lee, L., Dai, W., Xing, E.: Dual motion gan for future-flow embedded video prediction. In: International Conference on Computer Vision, pp. 1762\u20131770 (2017)","DOI":"10.1109\/ICCV.2017.194"},{"key":"10_CR5","unstructured":"Oh, J., Guo, X., Lee, H., Lewis, R.: Action-conditional video prediction using deep networks in atari games. In: Neural Information Process System, pp. 2863\u20132871 (2015)"},{"key":"10_CR6","unstructured":"Kalchbrenner, N., Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A.: Video pixel networks. In: International Conference on Machine Learning (2016)"},{"key":"10_CR7","unstructured":"Wang, Y., Lu, J., Yang, M.H., Li, L.J., Long, M.: Eidetic 3d lstm: A model for video prediction and beyond. In: International Conference on Machine Learning (2018)"},{"key":"10_CR8","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial networks. In: Advances in Neural Information Processing Systems, pp: 2672\u20132680 (2014)"},{"key":"10_CR9","unstructured":"Mathieu, M., Couprie, C., Lecun, Y.: Deep multi-scale video prediction beyond mean square error. In: International Conference on Machine Learning, pp: 1\u201314 (2016)"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: International Conference on Computer Vision, pp. 5908\u20135916 (2017)","DOI":"10.1109\/ICCV.2017.629"},{"key":"10_CR11","unstructured":"Ranzato, M.A., Szlam, A., Bruna, J., Mathieu, M., Collobert, R.: Video (language) modeling: a baseline for generative models of natural videos. In: Eprint Arxiv, pp: 1\u201315 (2014)"},{"key":"10_CR12","unstructured":"Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Advances in Neural Information Processing Systems, pp. 2377\u20132385 (2015)"},{"key":"10_CR13","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., WOO, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Neural Information Process System, pp. 802\u2013810 (2015)"},{"key":"10_CR14","unstructured":"Wang, Y., Long, M., et al.: Predrnn: recurrent neural networks for predictive learning using spatiotemporal lstms. In: Neural Information Process System, pp. 879\u2013888 (2017)"},{"key":"10_CR15","unstructured":"Wang, Y., Gao, Z., Long, M., Wang, J., Yu, P.: Predrnn++: towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In: International Conference on Machine Learning, pp. 5123\u20135132 (2018)"},{"key":"10_CR16","unstructured":"Lotter, W., et al.: Deep predictive coding networks for video prediction and unsupervised learning. In: International Conference on Learning Representations, pp. 4213\u20134223 (2016)"},{"key":"10_CR17","unstructured":"Villegas, R., Yang, J., et al.: Decomposing motion and content for natural video sequence prediction In: International Conference on Machine Learning, pp. 3560\u20133569 (2017)"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Sun, F., Li, S., et al.: Costnet: a concise overpass spatiotemporal network for predictive learning. In: ISPRS International Journal of Geo-Information, pp: 209\u2013220 (2020)","DOI":"10.3390\/ijgi9040209"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Oliu, M., Selva, J., Escalera, S.: Folded recurrent neural networks for future video prediction. In: European Conference on Computer Vision, pp. 716\u2013731 (2018)","DOI":"10.1007\/978-3-030-01264-9_44"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.: Effective approaches to attention-based neural machine translation. In: Computer ence, pp: 523\u2013530 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Subbarao, M., Tyan, J.K.: Selecting the optimal focus measure for autofocusing and depth-from-focus. In: Pattern Analysis and Machine Intelligence, pp: 864\u2013870 (1998)","DOI":"10.1109\/34.709612"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-02375-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:04:25Z","timestamp":1652180665000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-02375-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031023743","9783031023750"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-02375-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.acpr2021.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"154","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}