{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T04:08:40Z","timestamp":1751688520596,"version":"3.41.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319773797"},{"type":"electronic","value":"9783319773803"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-77380-3_25","type":"book-chapter","created":{"date-parts":[[2018,5,9]],"date-time":"2018-05-09T14:59:02Z","timestamp":1525877942000},"page":"258-268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Temporal Interval Regression Network for Video Action Detection"],"prefix":"10.1007","author":[{"given":"Qing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laiyun","family":"Qing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"25_CR1","doi-asserted-by":"crossref","unstructured":"Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., Gould, S.: Dynamic image networks for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3034\u20133042 (2016)","DOI":"10.1109\/CVPR.2016.331"},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Caba Heilbron, F., Carlos Niebles, J., Ghanem, B.: Fast temporal activity proposals for efficient detection of human actions in untrimmed videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1914\u20131923 (2016)","DOI":"10.1109\/CVPR.2016.211"},{"key":"25_CR3","unstructured":"Dan, O., Jakob, V., Cordelia, S.: The LEAR submission at Thumos 2014 (2014)"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"25_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2016.10.018","volume":"155","author":"H Idrees","year":"2017","unstructured":"Idrees, H., Zamir, A.R., Jiang, Y.G., Gorban, A., Laptev, I., Sukthankar, R., Shah, M.: The Thumos challenge on action recognition for videos \u201cin the wild\u201d. Comput. Vis. Image Underst. 155, 1\u201323 (2017)","journal-title":"Comput. Vis. Image Underst."},{"key":"25_CR7","unstructured":"Jaakkola, T.S., Haussler, D., et al.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems, pp. 487\u2013493 (1999)"},{"issue":"1","key":"25_CR8","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221\u2013231 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR9","unstructured":"Jiang, Y., Liu, J., Zamir, A.R., Toderici, G., Laptev, I., Shah, M., Sukthankar, R.: Thumos challenge: action recognition with a large number of classes (2014)"},{"key":"25_CR10","unstructured":"Kang, S., Wildes, R.P.: Review of action recognition and detection methods (2016). http:\/\/arxiv.org\/abs\/1610.06906"},{"key":"25_CR11","unstructured":"Limin, W., Yu, Q., Xiaoou, T.: Action recognition and detection by combining motion and appearance features (2014)"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Ma, S., Sigal, L., Sclaroff, S.: Learning activity progression in LSTMs for activity detection and early detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1942\u20131950 (2016)","DOI":"10.1109\/CVPR.2016.214"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Ni, B., Yang, X., Gao, S.: Progressively parsing interactional objects for fine grained action detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1020\u20131028 (2016)","DOI":"10.1109\/CVPR.2016.116"},{"key":"25_CR14","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91\u201399 (2015)"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Shou, Z., Wang, D., Chang, S.F.: Temporal action localization in untrimmed videos via multi-stage CNNs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1049\u20131058 (2016)","DOI":"10.1109\/CVPR.2016.119"},{"key":"25_CR16","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos, pp. 568\u2013576 (2014)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Singh, B., Marks, T.K., Jones, M., Tuzel, O., Shao, M.: A multi-stream Bi-directional recurrent neural network for fine-grained action detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1961\u20131970 (2016)","DOI":"10.1109\/CVPR.2016.216"},{"key":"25_CR18","unstructured":"Svebor, K., Lorenzo, S., Alberto, Del, B.: Fast saliency based pooling of fisher encoded dense trajectories (2014)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551\u20133558 (2013)","DOI":"10.1109\/ICCV.2013.441"},{"issue":"2","key":"25_CR21","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.cviu.2010.10.002","volume":"115","author":"D Weinland","year":"2011","unstructured":"Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115(2), 224\u2013241 (2011)","journal-title":"Comput. Vis. Image Underst."},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Yeung, S., Russakovsky, O., Mori, G., Fei-Fei, L.: End-to-end learning of action detection from frame glimpses in videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2678\u20132687 (2016)","DOI":"10.1109\/CVPR.2016.293"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Yu, G., Yuan, J.: Fast action proposals for human action detection and search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1302\u20131311 (2015)","DOI":"10.1109\/CVPR.2015.7298735"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Ng, J.Y.-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4694\u20134702 (2015)","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Newsam, S.: Efficient action detection in untrimmed videos via multi-task learning. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 197\u2013206. IEEE (2017)","DOI":"10.1109\/WACV.2017.29"}],"container-title":["Lecture Notes in Computer Science","Advances in Multimedia Information Processing \u2013 PCM 2017"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-77380-3_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T11:47:58Z","timestamp":1751629678000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-77380-3_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319773797","9783319773803"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-77380-3_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]}}}