{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:43:28Z","timestamp":1792629808137,"version":"4.5.0"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031200465","type":"print"},{"value":"9783031200472","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20047-2_14","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T06:02:55Z","timestamp":1666418575000},"page":"234-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Social-SSL: Self-supervised Cross-Sequence Representation Learning Based on\u00a0Transformers for\u00a0Multi-agent Trajectory Prediction"],"prefix":"10.1007","author":[{"given":"Li-Wu","family":"Tsao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-Siang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Han","family":"Shuai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lai-Kuan","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Huang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., et al.: Social LSTM: human trajectory prediction in crowded spaces. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961\u2013971 (2016)","DOI":"10.1109\/CVPR.2016.110"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Amirian, J., et al.: Social ways: learning multi-modal distributions of pedestrian trajectories with GANs. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2964\u20132972 (2019)","DOI":"10.1109\/CVPRW.2019.00359"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Choi, C., Choi, J.H., Li, J., Malla, S.: Shared cross-modal trajectory prediction for autonomous driving. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 244\u2013253 (2021)","DOI":"10.1109\/CVPR46437.2021.00031"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"14_CR5","unstructured":"Dong, L., et al.: Unified language model pre-training for natural language understanding and generation. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems (NeurIPS), pp. 13063\u201313075 (2019)"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Giuliari, F., Hasan, I., Cristani, M., Galasso, F.: Transformer networks for trajectory forecasting. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 10335\u201310342. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412190"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Gupta, A., et al.: Social GAN: socially acceptable trajectories with generative adversarial networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2255\u20132264 (2018)","DOI":"10.1109\/CVPR.2018.00240"},{"issue":"5","key":"14_CR8","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1103\/PhysRevE.51.4282","volume":"51","author":"D Helbing","year":"1995","unstructured":"Helbing, D., et al.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)","journal-title":"Phys. Rev. E"},{"issue":"8","key":"14_CR9","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., et al.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: STGAT: modeling spatial-temporal interactions for human trajectory prediction. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6271\u20136280 (2019)","DOI":"10.1109\/ICCV.2019.00637"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82(Series D), 35\u201345 (1960)","DOI":"10.1115\/1.3662552"},{"key":"14_CR12","unstructured":"Kipf, T.N., et al.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"14_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-642-33765-9_15","volume-title":"Computer Vision \u2013 ECCV 2012","author":"KM Kitani","year":"2012","unstructured":"Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M.: Activity forecasting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 201\u2013214. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33765-9_15"},{"key":"14_CR14","unstructured":"Kosaraju, V., et al.: Social-BIGAT: multimodal trajectory forecasting using bicycle-GAN and graph attention networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 137\u2013146 (2019)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Kuderer, M., et al.: Feature-based prediction of trajectories for socially compliant navigation. In: Robotics: Science and Systems (2012)","DOI":"10.15607\/RSS.2012.VIII.025"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Lerner, A., et al.: Crowds by example. Comput. Graph. Forum. 26, 655\u2013664. Wiley Online Library (2007)","DOI":"10.1111\/j.1467-8659.2007.01089.x"},{"key":"14_CR17","unstructured":"Li, J., et al.: EvolveGraph: multi-agent trajectory prediction with dynamic relational reasoning. In: 34th Conference on Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Li, L.L., et al.: End-to-end contextual perception and prediction with interaction transformer. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5784\u20135791 (2020)","DOI":"10.1109\/IROS45743.2020.9341392"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Liang, J., et al.: Peeking into the future: predicting future person activities and locations in videos. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5725\u20135734 (2019)","DOI":"10.1109\/CVPR.2019.00587"},{"key":"14_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-030-58601-0_17","volume-title":"Computer Vision \u2013 ECCV 2020","author":"J Liang","year":"2020","unstructured":"Liang, J., Jiang, L., Hauptmann, A.: SimAug: learning robust representations from simulation for trajectory prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 275\u2013292. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_17"},{"key":"14_CR21","unstructured":"Lipton, Z.C., et al.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)"},{"key":"14_CR22","unstructured":"Loshchilov, I., et al.: Decoupled weight decay regularization. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Mangalam, K., et al.: It is not the journey but the destination: endpoint conditioned trajectory prediction. In: European Conference on Computer Vision (ECCV), pp. 759\u2013776 (2020)","DOI":"10.1007\/978-3-030-58536-5_45"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"M\u00f8gelmose, A., et al.: Trajectory analysis and prediction for improved pedestrian safety: integrated framework and evaluations. In: IEEE Intelligent Vehicles Symposium (IV), pp. 330\u2013335. IEEE (2015)","DOI":"10.1109\/IVS.2015.7225707"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Mohamed, A., et al.: Social-STGCNN: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14424\u201314432 (2020)","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Mohseni, S., et al.: Self-supervised learning for generalizable out-of-distribution detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).,vol. 34, pp. 5216\u20135223 (2020)","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"14_CR27","unstructured":"Mohsenvand, M.N., et al.: Contrastive representation learning for electroencephalogram classification. In: Proceedings of Machine Learning for Health, vol. 136, pp. 238\u2013253. (PMLR) (2020)"},{"key":"14_CR28","unstructured":"Monti, A., et al.: Dag-Net: double attentive graph neural network for trajectory forecasting. In: 25th International Conference on Pattern Recognition (ICPR) (2020)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Pang, B., et al.: Trajectory prediction with latent belief energy-based model. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11814\u201311824 (2021)","DOI":"10.1109\/CVPR46437.2021.01164"},{"key":"14_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-58621-8_17","volume-title":"Computer Vision \u2013 ECCV 2020","author":"SH Park","year":"2020","unstructured":"Park, S.H., et al.: Diverse and admissible trajectory forecasting through multimodal context understanding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 282\u2013298. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_17"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Pellegrini, S., et al.: You\u2019ll never walk alone: Modeling social behavior for multi-target tracking. In: IEEE\/CVF 12th International Conference on Computer Vision (ICCV), pp. 261\u2013268 (2009)","DOI":"10.1109\/ICCV.2009.5459260"},{"key":"14_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-319-46484-8_33","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Robicquet","year":"2016","unstructured":"Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S.: Learning social etiquette: human trajectory understanding in crowded scenes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 549\u2013565. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_33"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., et al.: Sophie: an attentive GAN For predicting paths compliant to social and physical constraints. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1349\u20131358 (2019)","DOI":"10.1109\/CVPR.2019.00144"},{"key":"14_CR34","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., et al.: Car-Net: clairvoyant attentive recurrent network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 151\u2013167 (2018)","DOI":"10.1007\/978-3-030-01252-6_10"},{"key":"14_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/978-3-030-58523-5_40","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Salzmann","year":"2020","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: dynamically-feasible trajectory forecasting with heterogeneous data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 683\u2013700. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_40"},{"key":"14_CR36","doi-asserted-by":"crossref","unstructured":"Shi, L., et al.: Social interpretable tree for pedestrian trajectory prediction. In: AAAI Conference on Artificial Intelligence (AAAI) (2022)","DOI":"10.1609\/aaai.v36i2.20121"},{"key":"14_CR37","doi-asserted-by":"crossref","unstructured":"Shi, L., et al.: SGCN: sparse graph convolution network for pedestrian trajectory prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8994\u20139003 (2021)","DOI":"10.1109\/CVPR46437.2021.00888"},{"key":"14_CR38","doi-asserted-by":"crossref","unstructured":"Shi, X., et al.: Social DPF: socially acceptable distribution prediction of futures. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 35, pp. 2550\u20132557 (2021)","DOI":"10.1609\/aaai.v35i3.16357"},{"key":"14_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/978-3-030-58589-1_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Song","year":"2020","unstructured":"Song, H., Ding, W., Chen, Y., Shen, S., Wang, M.Y., Chen, Q.: PiP: planning-informed trajectory prediction for autonomous driving. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 598\u2013614. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_36"},{"key":"14_CR40","unstructured":"Song, K., et al.: Mass: Masked sequence to sequence pre-training for language generation. In: International Conference on Machine Learning (ICML), pp. 5926\u20135936 (2019)"},{"key":"14_CR41","unstructured":"Vaswani, A., et al.: Attention is all you need. In: 31st Conference on Advances Neural Information Processing Systems (NIPS 2017), pp. 5998\u20136008 (2017)"},{"issue":"2","key":"14_CR42","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1109\/TPAMI.2007.1167","volume":"30","author":"JM Wang","year":"2008","unstructured":"Wang, J.M., et al.: Gaussian process dynamical models for human motion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 283\u2013298 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-030-58610-2_30","volume-title":"Computer Vision \u2013 ECCV 2020","author":"C Yu","year":"2020","unstructured":"Yu, C., Ma, X., Ren, J., Zhao, H., Yi, S.: Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 507\u2013523. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_30"},{"key":"14_CR44","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Weng, X., Ou, Y., Kitani, K.M.: AgentFormer: agent-aware transformers for socio-temporal multi-agent forecasting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9813\u20139823 (2021)","DOI":"10.1109\/ICCV48922.2021.00967"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20047-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:10:35Z","timestamp":1792627835000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-20047-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200465","9783031200472"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20047-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"23 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}