{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:59:28Z","timestamp":1786978768045,"version":"3.56.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585167","type":"print"},{"value":"9783030585174","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58517-4_6","type":"book-chapter","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T15:03:11Z","timestamp":1602255791000},"page":"85-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Learning to Exploit Multiple Vision Modalities by Using Grafted Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5543-7619","authenticated-orcid":false,"given":"Yuhuang","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5479-1141","authenticated-orcid":false,"given":"Tobi","family":"Delbruck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-045X","authenticated-orcid":false,"given":"Shih-Chii","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,10,10]]},"reference":[{"issue":"10","key":"6_CR1","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1109\/TIP.2005.851684","volume":"14","author":"HA Aly","year":"2005","unstructured":"Aly, H.A., Dubois, E.: Image up-sampling using total-variation regularization with a new observation model. IEEE Trans. Image Process. 14(10), 1647\u20131659 (2005)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fnins.2018.00023","volume":"12","author":"J Anumula","year":"2018","unstructured":"Anumula, J., Neil, D., Delbruck, T., Liu, S.C.: Feature representations for neuromorphic audio spike streams. Front. Neurosci. 12, 23 (2018). https:\/\/doi.org\/10.3389\/fnins.2018.00023","journal-title":"Front. Neurosci."},{"issue":"8","key":"6_CR3","first-page":"1","volume":"20","author":"CH Bahnsen","year":"2018","unstructured":"Bahnsen, C.H., Moeslund, T.B.: Rain removal in traffic surveillance: does it matter? IEEE Trans. Intell. Transp. Syst. 20(8), 1\u201318 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"10","key":"6_CR4","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1109\/JSSC.2014.2342715","volume":"49","author":"C Brandli","year":"2014","unstructured":"Brandli, C., Berner, R., Yang, M., Liu, S.C., Delbruck, T.: A 240 $$\\times $$ 180 130 dB 3 $$\\mu $$s latency global shutter spatiotemporal vision sensor. IEEE J. Solid-State Circ. 49(10), 2333\u20132341 (2014)","journal-title":"IEEE J. Solid-State Circ."},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Hybrid task cascade for instance segmentation. In: 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00511"},{"key":"6_CR7","unstructured":"Chen, K., et al.: MMDetection: open MMLab detection toolbox and benchmark (2019). CoRR abs\/1906.07155"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Devaguptapu, C., Akolekar, N., Sharma, M.M., Balasubramanian, V.N.: Borrow from anywhere: pseudo multi-modal object detection in thermal imagery. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00135"},{"key":"6_CR9","unstructured":"Fisher, R.: CVonline: Image Databases (2020). http:\/\/homepages.inf.ed.ac.uk\/rbf\/CVonline\/Imagedbase.htm"},{"key":"6_CR10","unstructured":"FLIR: Free FLIR thermal dataset for algorithm training (2018). https:\/\/www.flir.com\/oem\/adas\/adas-dataset-form\/"},{"key":"6_CR11","unstructured":"Gallego, G., et al.: Event-based vision: a survey (2019). CoRR abs\/1904.08405"},{"key":"6_CR12","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, 07\u201309 July 2015, vol. 37, pp. 1180\u20131189. PMLR, Lille (2015)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414\u20132423 (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"6_CR14","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS Deep Learning and Representation Learning Workshop (2015)"},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"405","DOI":"10.3389\/fnins.2016.00405","volume":"10","author":"Y Hu","year":"2016","unstructured":"Hu, Y., Liu, H., Pfeiffer, M., Delbruck, T.: DVS benchmark datasets for object tracking, action recognition, and object recognition. Front. Neurosci. 10, 405 (2016)","journal-title":"Front. Neurosci."},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-319-46475-6_43","volume-title":"Computer Vision \u2013 ECCV 2016","author":"J Johnson","year":"2016","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694\u2013711. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_43"},{"key":"6_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2014)"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Kri\u0161to, M., Iva\u0161i\u0107-Kos, M.: Thermal imaging dataset for person detection. In: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1126\u20131131 (2019)","DOI":"10.23919\/MIPRO.2019.8757208"},{"issue":"7","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1109\/TPAMI.2016.2574707","volume":"39","author":"X Lagorce","year":"2017","unstructured":"Lagorce, X., Orchard, G., Galluppi, F., Shi, B.E., Benosman, R.B.: Hots: s hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1346\u20131359 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"6_CR20","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"2","key":"6_CR21","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","volume":"43","author":"P Lichtsteiner","year":"2008","unstructured":"Lichtsteiner, P., Posch, C., Delbruck, T.: A 128 $$\\times $$ 128 120 dB 15 $$\\mu $$s latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circ. 43(2), 566\u2013576 (2008)","journal-title":"IEEE J. Solid-State Circ."},{"key":"6_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"TY Lin","year":"2014","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Moeys, D.P., et al.: Steering a predator robot using a mixed frame\/event-driven convolutional neural network. In: 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), pp. 1\u20138 (2016)","DOI":"10.1109\/EBCCSP.2016.7605233"},{"issue":"10","key":"6_CR25","doi-asserted-by":"publisher","first-page":"2028","DOI":"10.1109\/TPAMI.2015.2392947","volume":"37","author":"G Orchard","year":"2015","unstructured":"Orchard, G., Meyer, C., Etienne-Cummings, R., Posch, C., Thakor, N., Benosman, R.: Hfirst: a temporal approach to object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(10), 2028\u20132040 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"6_CR26","doi-asserted-by":"publisher","first-page":"437","DOI":"10.3389\/fnins.2015.00437","volume":"9","author":"G Orchard","year":"2015","unstructured":"Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neurosci. 9, 437 (2015)","journal-title":"Front. Neurosci."},{"issue":"10","key":"6_CR27","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: Events-To-Video: bringing modern computer vision to event cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00398"},{"key":"6_CR29","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement (2018). arXiv"},{"key":"6_CR30","first-page":"91","volume-title":"Advances in Neural Information Processing Systems","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91\u201399. Curran Associates Inc., New York (2015)"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Rodin, C.D., de Lima, L.N., de Alcantara Andrade, F.A., Haddad, D.B., Johansen, T.A., Storvold, R.: Object classification in thermal images using convolutional neural networks for search and rescue missions with unmanned aerial systems. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2018)","DOI":"10.1109\/IJCNN.2018.8489465"},{"key":"6_CR32","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. In: International Conference on Laerning Representations (ICLR) (2015)"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R.E., Scaramuzza, D.: Fast image reconstruction with an event camera. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 156\u2013163 (2020)","DOI":"10.1109\/WACV45572.2020.9093366"},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: Hats: histograms of averaged time surfaces for robust event-based object classification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00186"},{"key":"6_CR35","unstructured":"Sun, Y., Tzeng, E., Darrell, T., Efros, A.A.: Unsupervised domain adaptation through self-supervision (2019)"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.754"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"You, K., Long, M., Cao, Z., Wang, J., Jordan, M.I.: Universal domain adaptation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00283"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"issue":"3","key":"6_CR39","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1109\/LRA.2018.2800793","volume":"3","author":"AZ Zhu","year":"2018","unstructured":"Zhu, A.Z., Thakur, D., \u00d6zaslan, T., Pfrommer, B., Kumar, V., Daniilidis, K.: The multivehicle stereo event camera dataset: an event camera dataset for 3d perception. IEEE Robot. Autom. Lett. 3(3), 2032\u20132039 (2018)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"6_CR40","doi-asserted-by":"crossref","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based learning of optical flow, depth, and egomotion. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00108"},{"key":"6_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-030-11024-6_54","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"AZ Zhu","year":"2019","unstructured":"Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K.: Unsupervised event-based optical flow using motion compensation. In: Leal-Taix\u00e9, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11134, pp. 711\u2013714. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11024-6_54"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58517-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T20:07:48Z","timestamp":1728418068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58517-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585167","9783030585174"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58517-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"10 October 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","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"}]}}