{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T22:11:05Z","timestamp":1726179065480},"publisher-location":"Cham","reference-count":68,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200526"},{"type":"electronic","value":"9783031200533"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-20053-3_13","type":"book-chapter","created":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:21:52Z","timestamp":1667665312000},"page":"214-234","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MENet: A Memory-Based Network with\u00a0Dual-Branch for\u00a0Efficient Event Stream Processing"],"prefix":"10.1007","author":[{"given":"Linhui","family":"Sun","sequence":"first","affiliation":[]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Hanqing","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,6]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Amir, A., et al.: A low power, fully event-based gesture recognition system. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 7388\u20137397. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.781","DOI":"10.1109\/CVPR.2017.781"},{"key":"13_CR2","doi-asserted-by":"publisher","unstructured":"Bardow, P., Davison, A.J., Leutenegger, S.: Simultaneous optical flow and intensity estimation from an event camera. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27\u201330 June 2016, pp. 884\u2013892. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.102","DOI":"10.1109\/CVPR.2016.102"},{"key":"13_CR3","unstructured":"Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., Andreopoulos, Y.: Graph-based spatial-temporal feature learning for neuromorphic vision sensing. CoRR abs\/1910.03579 (2019). http:\/\/arxiv.org\/abs\/1910.03579"},{"issue":"10","key":"13_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., Delbr\u00fcck, T.: A 240 $${\\times }$$ 180 130 db 3 $${\\mu }s$$ latency global shutter spatiotemporal vision sensor. IEEE J. Solid State Circuits 49(10), 2333\u20132341 (2014). https:\/\/doi.org\/10.1109\/JSSC.2014.2342715","journal-title":"IEEE J. Solid State Circuits"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Cai, Q., Pan, Y., Yao, T., Yan, C., Mei, T.: Memory matching networks for one-shot image recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 4080\u20134088. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00429","DOI":"10.1109\/CVPR.2018.00429"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.: Asynchronous convolutional networks for object detection in neuromorphic cameras. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 1656\u20131665. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00209","DOI":"10.1109\/CVPRW.2019.00209"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.: Attention mechanisms for object recognition with event-based cameras. In: IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, HI, USA, 7\u201311 January 2019, pp. 1127\u20131136. IEEE (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00125","DOI":"10.1109\/WACV.2019.00125"},{"key":"13_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-030-58565-5_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Cannici","year":"2020","unstructured":"Cannici, M., Ciccone, M., Romanoni, A., Matteucci, M.: A differentiable recurrent surface for asynchronous event-based data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12365, pp. 136\u2013152. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58565-5_9"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Chen, J., Meng, J., Wang, X., Yuan, J.: Dynamic graph CNN for event-camera based gesture recognition. In: IEEE International Symposium on Circuits and Systems, ISCAS 2020, Sevilla, Spain, 10\u201321 October 2020, pp. 1\u20135. IEEE (2020). https:\/\/doi.org\/10.1109\/ISCAS45731.2020.9181247","DOI":"10.1109\/ISCAS45731.2020.9181247"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Cheng, W., Luo, H., Yang, W., Yu, L., Chen, S., Li, W.: DET: a high-resolution DVS dataset for lane extraction. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 1666\u20131675. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00210","DOI":"10.1109\/CVPRW.2019.00210"},{"key":"13_CR11","doi-asserted-by":"publisher","unstructured":"Gallego, G., Rebecq, H., Scaramuzza, D.: A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 3867\u20133876. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00407","DOI":"10.1109\/CVPR.2018.00407"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Gehrig, D., Loquercio, A., Derpanis, K.G., Scaramuzza, D.: End-to-end learning of representations for asynchronous event-based data. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October\u20132 November 2019, pp. 5632\u20135642. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00573","DOI":"10.1109\/ICCV.2019.00573"},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/s11263-019-01209-w","volume":"128","author":"D Gehrig","year":"2019","unstructured":"Gehrig, D., Rebecq, H., Gallego, G., Scaramuzza, D.: Eklt: asynchronous photometric feature tracking using events and frames. Int. J. Comput. Vision 128, 601\u2013618 (2019)","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., van den Hengel, A.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), 27 October\u20132 November 2019, pp. 1705\u20131714. IEEE (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00179","DOI":"10.1109\/ICCV.2019.00179"},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3389\/fnins.2017.00309","volume":"11","author":"H Li","year":"2017","unstructured":"Li, H., Liu, H., Ji, X., Li, G., Shi, L.: Cifar10-dvs: an event-stream dataset for object classification. Front. Neurosci. 11, 309 (2017)","journal-title":"Front. Neurosci."},{"key":"13_CR16","unstructured":"He, W., et al.: Comparing snns and rnns on neuromorphic vision datasets: similarities and differences. CoRR abs\/2005.02183 (2020). https:\/\/arxiv.org\/abs\/2005.02183"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Huang, H., Yu, A., He, R.: Memory oriented transfer learning for semi-supervised image deraining. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, 19\u201325 June 2021, pp. 7732\u20137741. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00764"},{"key":"13_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1007\/978-3-030-69525-5_24","volume-title":"Computer Vision \u2013 ACCV 2020","author":"D Jack","year":"2021","unstructured":"Jack, D., Maire, F., Denman, S., Eriksson, A.: Sparse convolutions on continuous domains for point cloud and event stream networks. In: Ishikawa, H., Liu, C.-L., Pajdla, T., Shi, J. (eds.) ACCV 2020. LNCS, vol. 12622, pp. 400\u2013416. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69525-5_24"},{"key":"13_CR19","doi-asserted-by":"publisher","unstructured":"Jiang, Z., Zhang, Y., Zou, D., Ren, J.S.J., Lv, J., Liu, Y.: Learning event-based motion deblurring. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 3317\u20133326. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00338, https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Jiang_Learning_Event-Based_Motion_Deblurring_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.00338"},{"key":"13_CR20","unstructured":"Kaiser, L., Nachum, O., Roy, A., Bengio, S.: Learning to remember rare events. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=SJTQLdqlg"},{"key":"13_CR21","unstructured":"Khairallah, M.Z., Bonardi, F., Roussel, D., Bouchafa, S.: PCA event-based optical flow for visual odometry. CoRR abs\/2105.03760 (2021). https:\/\/arxiv.org\/abs\/2105.03760"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Khoei, M.A., Yousefzadeh, A., Pourtaherian, A., Moreira, O., Tapson, J.: Sparnet: sparse asynchronous neural network execution for energy efficient inference. In: 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020, Genova, Italy, 31 August\u20132 September 2020, pp. 256\u2013260. IEEE (2020). https:\/\/doi.org\/10.1109\/AICAS48895.2020.9073827","DOI":"10.1109\/AICAS48895.2020.9073827"},{"key":"13_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-3-319-46466-4_21","volume-title":"Computer Vision \u2013 ECCV 2016","author":"H Kim","year":"2016","unstructured":"Kim, H., Leutenegger, S., Davison, A.J.: Real-time 3D reconstruction and 6-DoF tracking with an event camera. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 349\u2013364. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_21"},{"key":"13_CR24","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"13_CR25","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images, pp. 32\u201333 (2009). https:\/\/www.cs.toronto.edu\/kriz\/learning-features-2009-TR.pdf"},{"key":"13_CR26","doi-asserted-by":"publisher","unstructured":"Kugele, A., Pfeil, T., Pfeiffer, M., Chicca, E.: Efficient processing of spatio-temporal data streams with spiking neural networks. Front. Neuroscie. 14, 439 (2020). https:\/\/doi.org\/10.3389\/fnins.2020.00439, https:\/\/www.frontiersin.org\/article\/10.3389\/fnins.2020.00439","DOI":"10.3389\/fnins.2020.00439"},{"issue":"7","key":"13_CR27","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.: HOTS: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1346\u20131359 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2574707","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"13_CR28","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). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, H.G., Choi, D.H., Kim, H., Ro, Y.M.: Video prediction recalling long-term motion context via memory alignment learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, 19\u201325 June 2021, pp. 3054\u20133063. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00307"},{"issue":"2","key":"13_CR30","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., Delbr\u00fcck, T.: A 128$${\\times }$$128 120 db 15 $${\\mu }s$$ latency asynchronous temporal contrast vision sensor. IEEE J. Solid State Circuits 43(2), 566\u2013576 (2008). https:\/\/doi.org\/10.1109\/JSSC.2007.914337","journal-title":"IEEE J. Solid State Circuits"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Q., Ruan, H., Xing, D., Tang, H., Pan, G.: Effective AER object classification using segmented probability-maximization learning in spiking neural networks. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7\u201312 February 2020, pp. 1308\u20131315. AAAI Press (2020). https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5486","DOI":"10.1609\/aaai.v34i02.5486"},{"key":"13_CR32","doi-asserted-by":"publisher","unstructured":"Manderscheid, J., Sironi, A., Bourdis, N., Migliore, D., Lepetit, V.: Speed invariant time surface for learning to detect corner points with event-based cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 10245\u201310254. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.01049","DOI":"10.1109\/CVPR.2019.01049"},{"key":"13_CR33","doi-asserted-by":"publisher","unstructured":"Maqueda, A.I., Loquercio, A., Gallego, G., Garc\u00eda, N., Scaramuzza, D.: Event-based vision meets deep learning on steering prediction for self-driving cars. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 5419\u20135427. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00568, http:\/\/openaccess.thecvf.com\/content_cvpr_2018\/html\/Maqueda_Event-Based_Vision_Meets_CVPR_2018_paper.html","DOI":"10.1109\/CVPR.2018.00568"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Massa, R., Marchisio, A., Martina, M., Shafique, M.: An efficient spiking neural network for recognizing gestures with a DVS camera on the loihi neuromorphic processor. CoRR abs\/2006.09985 (2020). https:\/\/arxiv.org\/abs\/2006.09985","DOI":"10.1109\/IJCNN48605.2020.9207109"},{"key":"13_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/978-3-030-58598-3_25","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Messikommer","year":"2020","unstructured":"Messikommer, N., Gehrig, D., Loquercio, A., Scaramuzza, D.: Event-based asynchronous sparse convolutional networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 415\u2013431. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_25"},{"key":"13_CR36","doi-asserted-by":"publisher","unstructured":"Miller, A.H., Fisch, A., Dodge, J., Karimi, A., Bordes, A., Weston, J.: Key-value memory networks for directly reading documents. In: Su, J., Carreras, X., Duh, K. (eds.) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, 1\u20134 November 2016, pp. 1400\u20131409. The Association for Computational Linguistics (2016). https:\/\/doi.org\/10.18653\/v1\/d16-1147","DOI":"10.18653\/v1\/d16-1147"},{"key":"13_CR37","doi-asserted-by":"publisher","unstructured":"Mitrokhin, A., Hua, Z., Ferm\u00fcller, C., Aloimonos, Y.: Learning visual motion segmentation using event surfaces. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 14402\u201314411. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01442","DOI":"10.1109\/CVPR42600.2020.01442"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Mueggler, E., Bartolozzi, C., Scaramuzza, D.: Fast event-based corner detection. In: British Machine Vision Conference 2017, BMVC 2017, London, UK, 4\u20137 September 2017. BMVA Press (2017). https:\/\/www.dropbox.com\/s\/vicqrsz0yicq65c\/0070.pdf?dl=1","DOI":"10.5244\/C.31.33"},{"issue":"12","key":"13_CR39","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1007\/s11263-018-1106-2","volume":"126","author":"G Munda","year":"2018","unstructured":"Munda, G., Reinbacher, C., Pock, T.: Real-time intensity-image reconstruction for event cameras using manifold regularisation. Int. J. Comput. Vision 126(12), 1381\u20131393 (2018). https:\/\/doi.org\/10.1007\/s11263-018-1106-2","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR40","doi-asserted-by":"publisher","unstructured":"Nguyen, A., Do, T., Caldwell, D.G., Tsagarakis, N.G.: Real-time 6dof pose relocalization for event cameras with stacked spatial LSTM networks. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 1638\u20131645. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00207","DOI":"10.1109\/CVPRW.2019.00207"},{"key":"13_CR41","doi-asserted-by":"publisher","unstructured":"Orchard, G., Benosman, R., Etienne-Cummings, R., Thakor, N.V.: A spiking neural network architecture for visual motion estimation. In: 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS), Rotterdam, The Netherlands, 31 October\u20132 November 2013, pp. 298\u2013301. IEEE (2013). https:\/\/doi.org\/10.1109\/BioCAS.2013.6679698","DOI":"10.1109\/BioCAS.2013.6679698"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Orchard, G., Jayawant, A., Cohen, G., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades (2015)","DOI":"10.3389\/fnins.2015.00437"},{"issue":"10","key":"13_CR43","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.V., Benosman, R.: Hfirst: a temporal approach to object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(10), 2028\u20132040 (2015). https:\/\/doi.org\/10.1109\/TPAMI.2015.2392947","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR44","doi-asserted-by":"publisher","unstructured":"Pan, L., Liu, M., Hartley, R.: Single image optical flow estimation with an event camera. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 1669\u20131678. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00174","DOI":"10.1109\/CVPR42600.2020.00174"},{"key":"13_CR45","doi-asserted-by":"publisher","unstructured":"Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., Dai, Y.: Bringing a blurry frame alive at high frame-rate with an event camera. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 6820\u20136829. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00698","DOI":"10.1109\/CVPR.2019.00698"},{"issue":"8","key":"13_CR46","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TPAMI.2019.2903179","volume":"42","author":"F Paredes-Vall\u00e9s","year":"2020","unstructured":"Paredes-Vall\u00e9s, F., Scheper, K.Y.W., de Croon, G.C.H.E.: Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: from events to global motion perception. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2051\u20132064 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2903179","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR47","doi-asserted-by":"publisher","unstructured":"Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 14360\u201314369. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01438, https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Park_Learning_Memory-Guided_Normality_for_Anomaly_Detection_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"13_CR48","doi-asserted-by":"publisher","unstructured":"Pei, W., Zhang, J., Wang, X., Ke, L., Shen, X., Tai, Y.: Memory-attended recurrent network for video captioning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 8347\u20138356. Computer Vision Foundation \/ IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00854, http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Pei_Memory-Attended_Recurrent_Network_for_Video_Captioning_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00854"},{"issue":"1","key":"13_CR49","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1109\/JSSC.2010.2085952","volume":"46","author":"C Posch","year":"2011","unstructured":"Posch, C., Matolin, D., Wohlgenannt, R.: A QVGA 143 db dynamic range frame-free PWM image sensor with lossless pixel-level video compression and time-domain CDS. IEEE J. Solid State Circuits 46(1), 259\u2013275 (2011). https:\/\/doi.org\/10.1109\/JSSC.2010.2085952","journal-title":"IEEE J. Solid State Circuits"},{"key":"13_CR50","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA, 4\u20139 December 2017, pp. 5099\u20135108 (2017)"},{"issue":"11","key":"13_CR51","doi-asserted-by":"publisher","first-page":"2767","DOI":"10.1109\/TPAMI.2019.2919301","volume":"42","author":"B Ramesh","year":"2020","unstructured":"Ramesh, B., Yang, H., Orchard, G., Thi, N.A.L., Zhang, S., Xiang, C.: DART: distribution aware retinal transform for event-based cameras. IEEE Trans. Pattern Anal. Mach. Intell. 42(11), 2767\u20132780 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2919301","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"13_CR52","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1007\/s11263-017-1050-6","volume":"126","author":"H Rebecq","year":"2017","unstructured":"Rebecq, H., Gallego, G., Mueggler, E., Scaramuzza, D.: EMVS: event-based multi-view stereo\u20143D reconstruction with an event camera in real-time. Int. J. Comput. Vision 126(12), 1394\u20131414 (2017). https:\/\/doi.org\/10.1007\/s11263-017-1050-6","journal-title":"Int. J. Comput. Vision"},{"key":"13_CR53","doi-asserted-by":"publisher","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, Long Beach, CA, USA, 16\u201320 June 2019, pp. 3857\u20133866. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00398","DOI":"10.1109\/CVPR.2019.00398"},{"issue":"6","key":"13_CR54","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1109\/TPAMI.2019.2963386","volume":"43","author":"H Rebecq","year":"2021","unstructured":"Rebecq, H., Ranftl, R., Koltun, V., Scaramuzza, D.: High speed and high dynamic range video with an event camera. IEEE Trans. Pattern Anal. Mach. Intell. 43(6), 1964\u20131980 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2019.2963386","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR55","doi-asserted-by":"publisher","unstructured":", Sekikawa, Y., Hara, K., Saito, H.: Eventnet: asynchronous recursive event processing. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 3887\u20133896. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00401","DOI":"10.1109\/CVPR.2019.00401"},{"key":"13_CR56","doi-asserted-by":"publisher","first-page":"19396","DOI":"10.1109\/ACCESS.2018.2823260","volume":"6","author":"C Shi","year":"2018","unstructured":"Shi, C., Li, J., Wang, Y., Luo, G.: Exploiting lightweight statistical learning for event-based vision processing. IEEE Access 6, 19396\u201319406 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2823260","journal-title":"IEEE Access"},{"key":"13_CR57","unstructured":"Shrestha, S.B., Orchard, G.: SLAYER: spike layer error reassignment in time. CoRR abs\/1810.08646 (2018). http:\/\/arxiv.org\/abs\/1810.08646"},{"key":"13_CR58","doi-asserted-by":"publisher","unstructured":"Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: HATS: histograms of averaged time surfaces for robust event-based object classification. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 1731\u20131740. Computer Vision Foundation\/IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00186","DOI":"10.1109\/CVPR.2018.00186"},{"key":"13_CR59","doi-asserted-by":"publisher","unstructured":"Wang, Q., Zhang, Y., Yuan, J., Lu, Y.: Space-time event clouds for gesture recognition: From RGB cameras to event cameras. In: IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, HI, USA, 7\u201311 January 2019, pp. 1826\u20131835. IEEE (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00199","DOI":"10.1109\/WACV.2019.00199"},{"key":"13_CR60","unstructured":"Weston, J., Chopra, S., Bordes, A.: Memory networks. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015). http:\/\/arxiv.org\/abs\/1410.3916"},{"key":"13_CR61","unstructured":"Wu, Z., Zhang, H., Lin, Y., Li, G., Wang, M., Tang, Y.: Liaf-net: leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing. CoRR abs\/2011.06176 (2020). https:\/\/arxiv.org\/abs\/2011.06176"},{"key":"13_CR62","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhou, H., Yang, B.: Graph-based asynchronous event processing for rapid object recognition. In: ICCV, pp. 934\u2013943 (2021)","DOI":"10.1109\/ICCV48922.2021.00097"},{"key":"13_CR63","doi-asserted-by":"publisher","unstructured":"Yang, J., Zhang, Q., Ni, B., Li, L., Liu, J., Zhou, M., Tian, Q.: Modeling point clouds with self-attention and gumbel subset sampling. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 3323\u20133332. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00344","DOI":"10.1109\/CVPR.2019.00344"},{"key":"13_CR64","doi-asserted-by":"crossref","unstructured":"Yao, M., et al.: Temporal-wise attention spiking neural networks for event streams classification. CoRR abs\/2107.11711 (2021). https:\/\/arxiv.org\/abs\/2107.11711","DOI":"10.1109\/ICCV48922.2021.01006"},{"key":"13_CR65","unstructured":"Zheng, H., Wu, Y., Deng, L., Hu, Y., Li, G.: Going deeper with directly-trained larger spiking neural networks. CoRR abs\/2011.05280 (2020). https:\/\/arxiv.org\/abs\/2011.05280"},{"issue":"5","key":"13_CR66","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1109\/TRO.2021.3062252","volume":"37","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Gallego, G., Shen, S.: Event-based stereo visual odometry. IEEE Trans. Rob. 37(5), 1433\u20131450 (2021). https:\/\/doi.org\/10.1109\/TRO.2021.3062252","journal-title":"IEEE Trans. Rob."},{"key":"13_CR67","doi-asserted-by":"publisher","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, Long Beach, CA, USA, 16\u201320 June 2019, pp. 989\u2013997. Computer Vision Foundation\/IEEE (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00108, http:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Zhu_Unsupervised_Event-Based_Learning_of_Optical_Flow_Depth_and_Egomotion_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00108"},{"key":"13_CR68","doi-asserted-by":"publisher","unstructured":"Zhu, L., Yang, Y.: Inflated episodic memory with region self-attention for long-tailed visual recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 4343\u20134352. Computer Vision Foundation\/IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00440","DOI":"10.1109\/CVPR42600.2020.00440"}],"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\/978-3-031-20053-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T16:26:42Z","timestamp":1667665602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20053-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200526","9783031200533"],"references-count":68,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20053-3_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 November 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)"}}]}}