{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:14:26Z","timestamp":1781018066774,"version":"3.54.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031500688","type":"print"},{"value":"9783031500695","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-50069-5_4","type":"book-chapter","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:02:34Z","timestamp":1705644154000},"page":"28-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TadML: A Fast Temporal Action Detection with\u00a0Mechanics-MLP"],"prefix":"10.1007","author":[{"given":"Bowen","family":"Deng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangliang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongchang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"4_CR1","unstructured":"Liu, X., Bai, S., Bai, X.: An empirical study of end-to-end temporal action detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 658\u2013666. IEEE, Long Beach (2019)"},{"issue":"8","key":"4_CR2","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1049\/ipr2.12481","volume":"16","author":"SG Ali","year":"2020","unstructured":"Ali, S.G., Ali, R.: Experimental protocol designed to employ Nd: YAG laser surgery for anterior chamber glaucoma detection via UBM. IET Image Proc. 16(8), 2171\u20132179 (2020)","journal-title":"IET Image Proc."},{"issue":"1","key":"4_CR3","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/TII.2021.3085669","volume":"18","author":"J Li","year":"2021","unstructured":"Li, J., Chen, J., Sheng, B., et al.: Automatic detection and classification system of domestic waste via multimodel cascaded convolutional neural network. IEEE Trans. Industr. Inf. 18(1), 163\u2013173 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Bahroun, S., Abed, R., Zagrouba, E.: Deep 3D-LBP: CNN-based fusion of shape modeling and texture descriptors for accurate face recognition. Vis. Comput. 1\u201316 (2021)","DOI":"10.1007\/s00371-021-02324-x"},{"issue":"8","key":"4_CR5","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.1007\/s00371-023-02999-4","volume":"39","author":"X Hu","year":"2023","unstructured":"Hu, X., Zheng, C., Huang, J., et al.: Cloth texture preserving image-based 3D virtual try-on. Vis. Comput. 39(8), 3347\u20133357 (2023)","journal-title":"Vis. Comput."},{"key":"4_CR6","doi-asserted-by":"publisher","unstructured":"Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: BSN: boundary sensitive network for temporal action proposal generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 3\u201321. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_1","DOI":"10.1007\/978-3-030-01225-0_1"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Lin, T., Liu, X., Li, X., Ding, E., Wen, S.: BMN: boundary-matching network for temporal action proposal generation. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3888\u20133897. IEEE, Seoul (2019)","DOI":"10.1109\/ICCV.2019.00399"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Lin, C., et al.: Learning salient boundary feature for anchor-free temporal action localization. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3319\u20133328. IEEE, Nashville (2021)","DOI":"10.1109\/CVPR46437.2021.00333"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 658\u2013666. IEEE, Long Beach (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Uijlings, J.R.R., Duta, I.C., Rostamzadeh, N., Sebe, N.: Realtime video classification using dense HOF\/HOG. In: Proceedings of International Conference on Multimedia Retrieval, pp. 145\u2013152. Association for Computing Machinery, New York (2014)","DOI":"10.1145\/2578726.2578744"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"Graves, A.: Long short-term memory. In: Graves, A. (ed.) Supervised Sequence Labelling with Recurrent Neural Networks, pp. 37\u201345. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24797-2_4","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Dai, C., Wei, Y., Xu, Z., Chen, M., Liu, Y., Fan, J.: An investigation into performance factors of two-stream I3D networks. In: 2021 26th International Conference on Automation and Computing (ICAC), pp. 1\u20136 (2021)","DOI":"10.23919\/ICAC50006.2021.9594206"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Zeng, R., et al.: Graph convolutional networks for tmporal action localization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4560\u20134570. IEEE, Seoul (2019)","DOI":"10.1109\/ICCV.2019.00719"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Zhang, D.J., et al.: MorphMLP: an efficient MLP-Like backbone for spatial-temporal representation learning. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13695, pp. 230\u2013248. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19833-5_14","DOI":"10.1007\/978-3-031-19833-5_14"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Tang, Y., et al.: An image patch is a wave: phase-aware vision MLP. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10925\u201310934. IEEE, New Orleans (2022)","DOI":"10.1109\/CVPR52688.2022.01066"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Idrees, H., et al.: The THUMOS challenge on action recognition for videos \u201cin the wild.\u201d. Comput. Vis. Image Underst. 155, 1\u201323 (2017)","DOI":"10.1016\/j.cviu.2016.10.018"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Heilbron, F.C., Escorcia, V., Ghanem, B., Niebles, J.C.: ActivityNet: a large-scale video benchmark for human activity understanding. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 961\u2013970 (2015)","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Yang, K., Qiao, P., Li, D., Lv, S., Dou, Y.: Exploring temporal preservation networks for precise temporal action localization. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 7477\u20137484. AAAI Press, New Orleans (2018)","DOI":"10.1609\/aaai.v32i1.12234"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Dai, X., Singh, B., Zhang, G., Davis, L.S., Chen, Y.Q.: Temporal context network for activity localization in videos. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5727\u20135736. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.610"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Gao, J., Yang, Z., Sun, C., Chen, K., Nevatia, R.: TURN TAP: temporal unit regression network for temporal action proposals. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3648\u20133656. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.392"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Xu, H., Das, A., Saenko, K.: R-C3D: region convolutional 3D network for temporal activity detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5794\u20135803. IEEE, Venice (2017)","DOI":"10.1109\/ICCV.2017.617"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ma, L., Zhang, Y., Liu, W., Chang, S.-F.: Multi-granularity generator for temporal action proposal. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3599\u20133608. IEEE, Long Beach (2019)","DOI":"10.1109\/CVPR.2019.00372"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Song, Q., Zhou, Y., Hu, M., Liu, C.: Faster learning of temporal action proposal via sparse multilevel boundary generator. Multim. Tools Appl. (2023)","DOI":"10.1007\/s11042-023-15308-x"},{"key":"4_CR24","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3390\/jimaging8080207","volume":"8","author":"S Sooksatra","year":"2022","unstructured":"Sooksatra, S., Watcharapinchai, S.: A comprehensive review on temporal-action proposal generation. J. Imaging 8, 207 (2022)","journal-title":"J. Imaging"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Chao, Y.-W., Vijayanarasimhan, S., Seybold, B., Ross, D.A., Deng, J., Sukthankar, R.: Rethinking the faster R-CNN architecture for temporal action localization. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1130\u20131139. IEEE, Salt Lake (2018)","DOI":"10.1109\/CVPR.2018.00124"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Xu, M., Zhao, C., Rojas, D.S., Thabet, A., Ghanem, B.: G-TAD: sub-graph localization for temporal action detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10153\u201310162. IEEE, Seattle (2020)","DOI":"10.1109\/CVPR42600.2020.01017"},{"key":"4_CR27","doi-asserted-by":"publisher","unstructured":"Zhao, P., Xie, L., Ju, C., Zhang, Y., Wang, Y., Tian, Q.: Bottom-up temporal action lcalization with mutual regularization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 539\u2013555. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_32","DOI":"10.1007\/978-3-030-58598-3_32"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Tang, W., Wang, L., Zheng, N., Hua, G.: Enriching local and global contexts for temporal action localization. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 13496\u201313505. IEEE, Montreal (2021)","DOI":"10.1109\/ICCV48922.2021.01326"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"5980","DOI":"10.1109\/TIP.2020.2987169","volume":"29","author":"P Dai","year":"2020","unstructured":"Dai, P., Li, Z., Zhang, Y., Liu, S., Zeng, B.: PBR-Net: imitating physically based rndering using deep neural network. IEEE Trans. Image Process. 29, 5980\u20135992 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"4_CR30","doi-asserted-by":"publisher","first-page":"8535","DOI":"10.1109\/TIP.2020.3016486","volume":"29","author":"L Yang","year":"2020","unstructured":"Yang, L., Peng, H., Zhang, D., Fu, J., Han, J.: Revisiting anchor mechanisms for temporal action localization. IEEE Trans. Image Process. 29, 8535\u20138548 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"4_CR31","doi-asserted-by":"crossref","unstructured":"Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., Mei, T.: Gaussian temporal awareness networks for action localization. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 344\u2013353 (2019)","DOI":"10.1109\/CVPR.2019.00043"},{"key":"4_CR32","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s11263-019-01211-2","volume":"128","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. Int. J. Comput. Vis. 128, 74\u201395 (2020)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50069-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:03:20Z","timestamp":1705644200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50069-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031500688","9783031500695"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50069-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2023","order":10,"name":"conference_id","label":"Conference ID","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"385","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":"149","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":"39% - 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":"3","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)"}}]}}