{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T20:08:02Z","timestamp":1725998882815},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030038007"},{"type":"electronic","value":"9783030038014"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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\/978-3-030-03801-4_25","type":"book-chapter","created":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T12:40:49Z","timestamp":1541767249000},"page":"274-285","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Instance-level Object Recognition Using Deep Temporal Coherence"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-1502-7818","authenticated-orcid":false,"given":"Miguel","family":"Lagunes-Fortiz","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8804-6238","authenticated-orcid":false,"given":"Dima","family":"Damen","sequence":"additional","affiliation":[]},{"given":"Walterio","family":"Mayol-Cuevas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,10]]},"reference":[{"issue":"3","key":"25_CR1","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","volume":"110","author":"H Bay","year":"2008","unstructured":"Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346\u2013359 (2008). https:\/\/doi.org\/10.1016\/j.cviu.2007.09.014","journal-title":"Comput. Vis. Image Underst."},{"key":"25_CR2","unstructured":"Canziani, A., Culurciello, E.: Cortexnet: a generic network family for robust visual temporal representations. CoRR abs\/1706.02735 (2017). http:\/\/arxiv.org\/abs\/1706.02735"},{"issue":"12","key":"25_CR3","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","volume":"54","author":"G Cheng","year":"2016","unstructured":"Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images. IEEE Trans. Geosci. Remote. Sens. 54(12), 7405\u20137415 (2016). https:\/\/doi.org\/10.1109\/TGRS.2016.2601622","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"25_CR4","unstructured":"Clark, R., Wang, S., Markham, A., Trigoni, N., Wen, H.: Vidloc: 6-dof video-clip relocalization. CoRR abs\/1702.06521 (2017). http:\/\/arxiv.org\/abs\/1702.06521"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Damen, D., Bunnun, P., Calway, A., Mayol-Cuevas, W.: Real-time learning and detection of 3D texture-less objects: a scalable approach. In: British Machine Vision Conference. BMVA, September 2012. http:\/\/www.cs.bris.ac.uk\/Publications\/Papers\/2001575.pdf","DOI":"10.5244\/C.26.23"},{"key":"25_CR6","unstructured":"Donahue, J., et al.: Decaf: A deep convolutional activation feature for generic visual recognition. CoRR abs\/1310.1531 (2013). http:\/\/arxiv.org\/abs\/1310.1531"},{"key":"25_CR7","unstructured":"Fawzi, A., Moosavi-Dezfooli, S., Frossard, P.: Robustness of classifiers: from adversarial to random noise. CoRR abs\/1608.08967 (2016). http:\/\/arxiv.org\/abs\/1608.08967"},{"key":"25_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/11550907_126","volume-title":"Artificial Neural Networks: Formal Models and Their Applications \u2013 ICANN 2005","author":"A Graves","year":"2005","unstructured":"Graves, A., Fern\u00e1ndez, S., Schmidhuber, J.: Bidirectional LSTM networks for improved phoneme classification and recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadro\u017cny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 799\u2013804. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11550907_126"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Graves, A., Jaitly, N., Mohamed, A.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic, December 8\u201312, 2013, pp. 273\u2013278 (2013). https:\/\/doi.org\/10.1109\/ASRU.2013.6707742","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"25_CR10","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3D residual networks for action recognition. CoRR abs\/1708.07632 (2017). http:\/\/arxiv.org\/abs\/1708.07632"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Hoda\u0148, T., Haluza, P., Obdr\u017e\u00e1lek, \u0160., Matas, J., Lourakis, M., Zabulis, X.: T-LESS: An RGB-D dataset for 6D pose estimation of texture-less objects. In: IEEE Winter Conference on Applications of Computer Vision (WACV) (2017)","DOI":"10.1109\/WACV.2017.103"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Speed\/accuracy trade-offs for modern convolutional object detectors. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.351"},{"issue":"1","key":"25_CR13","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). https:\/\/doi.org\/10.1109\/TPAMI.2012.59","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR14","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. CoRR abs\/1606.09282 (2016). http:\/\/arxiv.org\/abs\/1606.09282"},{"key":"25_CR15","unstructured":"Lomonaco, V., Maltoni, D.: Core50: a new dataset and benchmark for continuous object recognition. arXiv preprint arXiv:1705.03550 (2017)"},{"issue":"2","key":"25_CR16","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91\u2013110 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int. J. Comput. Vision"},{"key":"25_CR17","doi-asserted-by":"publisher","unstructured":"Osherov, E., Lindenbaum, M.: Increasing cnn robustness to occlusions by reducing filter support. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 550\u2013561, October 2017. https:\/\/doi.org\/10.1109\/ICCV.2017.67","DOI":"10.1109\/ICCV.2017.67"},{"key":"25_CR18","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR abs\/1602.07261 (2016). http:\/\/arxiv.org\/abs\/1602.07261"},{"key":"25_CR19","unstructured":"Tripathi, S., Lipton, Z.C., Belongie, S.J., Nguyen, T.Q.: Context matters: refining object detection in video with recurrent neural networks. CoRR abs\/1607.04648 (2016). http:\/\/arxiv.org\/abs\/1607.04648"},{"key":"25_CR20","unstructured":"Zamir, A.R., Wu, T., Sun, L., Shen, W., Malik, J., Savarese, S.: Feedback networks. CoRR abs\/1612.09508 (2016). http:\/\/arxiv.org\/abs\/1612.09508"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Computing"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03801-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T06:51:08Z","timestamp":1572591068000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-03801-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030038007","9783030038014"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03801-4_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"ISVC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Visual Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Las Vegas, NV","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2018","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":"isvc2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isvc.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"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"113","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"66","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"58% - 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"}},{"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"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"54 papers were accepted for the main symposium out of 91 submissions; and 12 papers were accepted for the special tracks out of 22 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}
