{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:42:04Z","timestamp":1776886924418,"version":"3.51.2"},"publisher-location":"Cham","reference-count":82,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031731150","type":"print"},{"value":"9783031731167","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73116-7_2","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:15:38Z","timestamp":1730301338000},"page":"19-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Spiking Wavelet Transformer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0228-9082","authenticated-orcid":false,"given":"Yuetong","family":"Fang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8940-0461","authenticated-orcid":false,"given":"Ziqing","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0696-4363","authenticated-orcid":false,"given":"Lingfeng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiahang","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Honglei","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0792-8974","authenticated-orcid":false,"given":"Renjing","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"2_CR1","unstructured":"Auge, D., Mueller, E.: Resonate-and-fire neurons as frequency selective input encoders for spiking neural networks (2020)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Basu, A., Deng, L., Frenkel, C., Zhang, X.: Spiking neural network integrated circuits: a review of trends and future directions. In: 2022 IEEE Custom Integrated Circuits Conference (CICC), pp.\u00a01\u20138. IEEE (2022)","DOI":"10.1109\/CICC53496.2022.9772783"},{"key":"2_CR3","unstructured":"Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., Andreopoulos, Y.: Graph-based object classification for neuromorphic vision sensing. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 491\u2013501 (2019)","DOI":"10.1109\/ICCV.2019.00058"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Bochner, S., Chandrasekharan, K.: Fourier transforms, No.\u00a019. Princeton University Press (1949)","DOI":"10.1515\/9781400882243"},{"key":"2_CR6","unstructured":"Bu, T., Fang, W., Ding, J., Dai, P., Yu, Z., Huang, T.: Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks. In: International Conference on Learning Representations (2021)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Burkitt, A.N.: A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biol. Cybern. 95, 1\u201319 (2006)","DOI":"10.1007\/s00422-006-0068-6"},{"issue":"1","key":"2_CR8","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s11263-014-0788-3","volume":"113","author":"Y Cao","year":"2015","unstructured":"Cao, Y., Chen, Y., Khosla, D.: Spiking deep convolutional neural networks for energy-efficient object recognition. Int. J. Comput. Vision 113(1), 54\u201366 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Chen, S., Ye, T., Bai, J., Chen, E., Shi, J., Zhu, L.: Sparse sampling transformer with uncertainty-driven ranking for unified removal of raindrops and rain streaks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13106\u201313117 (2023)","DOI":"10.1109\/ICCV51070.2023.01205"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Chen, S., et al.: MSP-former: Multi-scale projection transformer for single image desnowing. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10095496"},{"key":"2_CR11","unstructured":"Dao, T., et al.: Monarch: expressive structured matrices for efficient and accurate training. In: International Conference on Machine Learning, pp. 4690\u20134721. PMLR (2022)"},{"issue":"1","key":"2_CR12","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MM.2018.112130359","volume":"38","author":"M Davies","year":"2018","unstructured":"Davies, M., et al.: Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82\u201399 (2018)","journal-title":"IEEE Micro"},{"issue":"5","key":"2_CR13","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/JPROC.2021.3067593","volume":"109","author":"M Davies","year":"2021","unstructured":"Davies, M., et al.: Advancing neuromorphic computing with loihi: A survey of results and outlook. Proc. IEEE 109(5), 911\u2013934 (2021)","journal-title":"Proc. IEEE"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2_CR15","unstructured":"Deng, S., Li, Y., Zhang, S., Gu, S.: Temporal efficient training of spiking neural network via gradient re-weighting. arXiv preprint arXiv:2202.11946 (2022)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Ding, J., Yu, Z., Tian, Y., Huang, T.: Optimal ANN-SNN conversion for fast and accurate inference in deep spiking neural networks. arXiv preprint arXiv:2105.11654 (2021)","DOI":"10.24963\/ijcai.2021\/321"},{"key":"2_CR17","unstructured":"Duan, C., Ding, J., Chen, S., Yu, Z., Huang, T.: Temporal effective batch normalization in spiking neural networks. In: Advances in Neural Information Processing Systems, vol. 35, pp. 34377\u201334390 (2022)"},{"key":"2_CR18","unstructured":"Fang, W., Yu, Z., Chen, Y., Huang, T., Masquelier, T., Tian, Y.: Deep residual learning in spiking neural networks. In: Advances in Neural Information Processing Systems, vol. 34, pp. 21056\u201321069 (2021)"},{"issue":"10","key":"2_CR19","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1007\/s11265-022-01772-5","volume":"94","author":"EP Frady","year":"2022","unstructured":"Frady, E.P., et al.: Efficient neuromorphic signal processing with resonator neurons. J. Signal Process. Syst. 94(10), 917\u2013927 (2022)","journal-title":"J. Signal Process. Syst."},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Gaudart, L., Crebassa, J., Petrakian, J.P.: Wavelet transform in human visual channels. Appl. Opt. 32(22), 4119\u20134127 (1993)","DOI":"10.1364\/AO.32.004119"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Gu, P., Xiao, R., Pan, G., Tang, H.: STCA: spatio-temporal credit assignment with delayed feedback in deep spiking neural networks. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. pp. 1366\u20131372. International Joint Conferences on Artificial Intelligence Organization, Macao, China (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/189","DOI":"10.24963\/ijcai.2019\/189"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Guo, Y., Zhang, L., Chen, Y., Tong, X., Liu, X., Wang, Y., Huang, X., Ma, Z.: Real spike: Learning real-valued spikes for spiking neural networks. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, pp. 52\u201368. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19775-8_4","DOI":"10.1007\/978-3-031-19775-8_4"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"He, C., et al.: Camouflaged object detection with feature decomposition and edge reconstruction. In: CVPR, pp. 22046\u201322055 (2023)","DOI":"10.1109\/CVPR52729.2023.02111"},{"key":"2_CR24","unstructured":"He, C., Li, K., Zhang, Y., Xu, G., Tang, L.: Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping. In: NeurIPS (2024)"},{"key":"2_CR25","unstructured":"He, C., et al.: Diffusion models in low-level vision: a survey. arXiv preprint arXiv:2406.11138 (2024)"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"issue":"4","key":"2_CR28","doi-asserted-by":"publisher","first-page":"20180007","DOI":"10.1098\/rsfs.2018.0007","volume":"8","author":"M Hopkins","year":"2018","unstructured":"Hopkins, M., Pineda-Garcia, G., Bogdan, P.A., Furber, S.B.: Spiking neural networks for computer vision. Interface Focus 8(4), 20180007 (2018)","journal-title":"Interface Focus"},{"issue":"8","key":"2_CR29","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1109\/TNNLS.2021.3119238","volume":"34","author":"Y Hu","year":"2021","unstructured":"Hu, Y., Tang, H., Pan, G.: Spiking deep residual networks. IEEE Trans. Neural Netw. Learn. Syst. 34(8), 5200\u20135205 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR30","unstructured":"Hu, Y., Deng, L., Wu, Y., Yao, M., Li, G.: Advancing spiking neural networks towards deep residual learning. arXiv preprint arXiv:2112.08954 (2021)"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Hu, Y., Deng, L., Wu, Y., Yao, M., Li, G.: Advancing spiking neural networks toward deep residual learning. IEEE Trans. Neural Netw. Learn. Syst. (2024)","DOI":"10.1109\/TNNLS.2024.3355393"},{"key":"2_CR32","doi-asserted-by":"publisher","first-page":"1123698","DOI":"10.3389\/fnins.2023.1123698","volume":"17","author":"M Ji","year":"2023","unstructured":"Ji, M., Wang, Z., Yan, R., Liu, Q., Xu, S., Tang, H.: SCTN: event-based object tracking with energy-efficient deep convolutional spiking neural networks. Front. Neurosci. 17, 1123698 (2023)","journal-title":"Front. Neurosci."},{"issue":"4","key":"2_CR33","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/TNNLS.2016.2583223","volume":"28","author":"A Jim\u00e9nez-Fern\u00e1ndez","year":"2016","unstructured":"Jim\u00e9nez-Fern\u00e1ndez, A., et al.: A binaural neuromorphic auditory sensor for FPGA: a spike signal processing approach. IEEE Trans. Neural Netw. Learn. Syst. 28(4), 804\u2013818 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"2_CR34","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)"},{"key":"2_CR35","doi-asserted-by":"publisher","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","DOI":"10.1109\/5.726791"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Lee, D., Yin, R., Kim, Y., Moitra, A., Li, Y., Panda, P.: TT-SNN: tensor train decomposition for efficient spiking neural network training. arXiv preprint arXiv:2401.08001 (2024)","DOI":"10.23919\/DATE58400.2024.10546679"},{"key":"2_CR37","unstructured":"Lee, I., Kim, J., Kim, Y., Kim, S., Park, G., Park, K.T.: Wavelet transform image coding using human visual system. In: Proceedings of APCCAS\u201994-1994 Asia Pacific Conference on Circuits and Systems, pp. 619\u2013623. IEEE (1994)"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Li, H., Liu, H., Ji, X., Li, G., Shi, L.: CIFAR10-DVS: an event-stream dataset for object classification. Front. Neurosci. 11 (2017)","DOI":"10.3389\/fnins.2017.00309"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Li, Q., Shen, L., Guo, S., Lai, Z.: Wavelet integrated CNNs for noise-robust image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7245\u20137254 (2020)","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"2_CR40","doi-asserted-by":"publisher","unstructured":"Li, Y., Kim, Y., Park, H., Geller, T., Panda, P.: Neuromorphic data augmentation for training spiking neural networks. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, pp. 631\u2013649. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20071-7_37","DOI":"10.1007\/978-3-031-20071-7_37"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Li, Y., Kim, Y., Park, H., Geller, T., Panda, P.: Neuromorphic data augmentation for training spiking neural networks. arXiv preprint arXiv:2203.06145 (2022)","DOI":"10.1007\/978-3-031-20071-7_37"},{"key":"2_CR42","doi-asserted-by":"publisher","unstructured":"Liu, Q., Xing, D., Tang, H., Ma, D., Pan, G.: Event-based action recognition using motion information and spiking neural networks. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, pp. 1743\u20131749. International Joint Conferences on Artificial Intelligence Organization, Montreal, Canada (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/240","DOI":"10.24963\/ijcai.2021\/240"},{"key":"2_CR43","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2021.688344","volume":"15","author":"J L\u00f3pez-Randulfe","year":"2021","unstructured":"L\u00f3pez-Randulfe, J., Duswald, T., Bing, Z., Knoll, A.: Spiking neural network for Fourier transform and object detection for automotive radar. Front. Neurorobot. 15, 688344 (2021)","journal-title":"Front. Neurorobot."},{"issue":"11","key":"2_CR44","doi-asserted-by":"publisher","first-page":"2792","DOI":"10.1109\/TC.2022.3162708","volume":"71","author":"J L\u00f3pez-Randulfe","year":"2022","unstructured":"L\u00f3pez-Randulfe, J., et al.: Time-coded spiking Fourier transform in neuromorphic hardware. IEEE Trans. Comput. 71(11), 2792\u20132802 (2022)","journal-title":"IEEE Trans. Comput."},{"issue":"9","key":"2_CR45","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659\u20131671 (1997)","journal-title":"Neural Netw."},{"key":"2_CR46","doi-asserted-by":"publisher","first-page":"275","DOI":"10.3389\/fnins.2020.00275","volume":"14","author":"JM Maro","year":"2020","unstructured":"Maro, J.M., Ieng, S.H., Benosman, R.: Event-based gesture recognition with dynamic background suppression using smartphone computational capabilities. Front. Neurosci. 14, 275 (2020)","journal-title":"Front. Neurosci."},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Meng, Q., Xiao, M., Yan, S., Wang, Y., Lin, Z., Luo, Z.Q.: Training high-performance low-latency spiking neural networks by differentiation on spike representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12444\u201312453 (2022)","DOI":"10.1109\/CVPR52688.2022.01212"},{"key":"2_CR48","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.neunet.2022.06.001","volume":"153","author":"Q Meng","year":"2022","unstructured":"Meng, Q., Yan, S., Xiao, M., Wang, Y., Lin, Z., Luo, Z.Q.: Training much deeper spiking neural networks with a small number of time-steps. Neural Netw. 153, 254\u2013268 (2022)","journal-title":"Neural Netw."},{"issue":"6197","key":"2_CR49","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1126\/science.1254642","volume":"345","author":"PA Merolla","year":"2014","unstructured":"Merolla, P.A., et al.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668\u2013673 (2014)","journal-title":"Science"},{"key":"2_CR50","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3389\/fnbot.2019.00038","volume":"13","author":"S Miao","year":"2019","unstructured":"Miao, S., et al.: Neuromorphic vision datasets for pedestrian detection, action recognition, and fall detection. Front. Neurorobot. 13, 38 (2019)","journal-title":"Front. Neurorobot."},{"key":"2_CR51","doi-asserted-by":"crossref","unstructured":"Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Front. Neuroscience 9 (2015)","DOI":"10.3389\/fnins.2015.00437"},{"key":"2_CR52","unstructured":"Park, N., Kim, S.: How do vision transformers work? arXiv preprint arXiv:2202.06709 (2022)"},{"issue":"7767","key":"2_CR53","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-019-1424-8","volume":"572","author":"J Pei","year":"2019","unstructured":"Pei, J., et al.: Towards artificial general intelligence with hybrid tianjic chip architecture. Nature 572(7767), 106\u2013111 (2019)","journal-title":"Nature"},{"issue":"5","key":"2_CR54","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1038\/s42256-022-00480-w","volume":"4","author":"A Rao","year":"2022","unstructured":"Rao, A., Plank, P., Wild, A., Maass, W.: A long short-term memory for AI applications in spike-based neuromorphic hardware. Nature Mach. Intell. 4(5), 467\u2013479 (2022)","journal-title":"Nature Mach. Intell."},{"issue":"12","key":"2_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571155","volume":"55","author":"N Rathi","year":"2023","unstructured":"Rathi, N., et al.: Exploring neuromorphic computing based on spiking neural networks: algorithms to hardware. ACM Comput. Surv. 55(12), 1\u201349 (2023)","journal-title":"ACM Comput. Surv."},{"key":"2_CR56","unstructured":"Rathi, N., Srinivasan, G., Panda, P., Roy, K.: Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. arXiv preprint arXiv:2005.01807 (2020)"},{"issue":"7784","key":"2_CR57","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","volume":"575","author":"K Roy","year":"2019","unstructured":"Roy, K., Jaiswal, A., Panda, P.: Towards spike-based machine intelligence with neuromorphic computing. Nature 575(7784), 607\u2013617 (2019)","journal-title":"Nature"},{"issue":"1","key":"2_CR58","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s43588-021-00184-y","volume":"2","author":"CD Schuman","year":"2022","unstructured":"Schuman, C.D., Kulkarni, S.R., Parsa, M., Mitchell, J.P., Kay, B., et al.: Opportunities for neuromorphic computing algorithms and applications. Nature Comput. Sci. 2(1), 10\u201319 (2022)","journal-title":"Nature Comput. Sci."},{"key":"2_CR59","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"2_CR60","doi-asserted-by":"crossref","unstructured":"Shen, S., Zhao, D., Shen, G., Zeng, Y.: TIM: an efficient temporal interaction module for spiking transformer. arXiv preprint arXiv:2401.11687 (2024)","DOI":"10.24963\/ijcai.2024\/347"},{"key":"2_CR61","unstructured":"Si, C., Yu, W., Zhou, P., Zhou, Y., Wang, X., Yan, S.: Inception transformer. In: Advances in Neural Information Processing Systems, vol. 35, pp. 23495\u201323509 (2022)"},{"key":"2_CR62","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1731\u20131740 (2018)","DOI":"10.1109\/CVPR.2018.00186"},{"issue":"4","key":"2_CR63","doi-asserted-by":"publisher","DOI":"10.1088\/2634-4386\/ac8828","volume":"2","author":"KM Stewart","year":"2022","unstructured":"Stewart, K.M., Neftci, E.O.: Meta-learning spiking neural networks with surrogate gradient descent. Neuromorphic Comput. Eng. 2(4), 044002 (2022)","journal-title":"Neuromorphic Comput. Eng."},{"key":"2_CR64","doi-asserted-by":"crossref","unstructured":"Su, Q., et al.: Deep directly-trained spiking neural networks for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6555\u20136565 (2023)","DOI":"10.1109\/ICCV51070.2023.00603"},{"key":"2_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115783","volume":"404","author":"T Tripura","year":"2023","unstructured":"Tripura, T., Chakraborty, S.: Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems. Comput. Methods Appl. Mech. Eng. 404, 115783 (2023)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"2_CR66","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"2_CR67","doi-asserted-by":"crossref","unstructured":"Viale, A., Marchisio, A., Martina, M., Masera, G., Shafique, M.: CarSNN: an efficient spiking neural network for event-based autonomous cars on the Loihi neuromorphic research processor. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533738"},{"key":"2_CR68","unstructured":"Wang, Z., Fang, Y., Cao, J., Xu, R.: Bursting spikes: efficient and high-performance SNNs for event-based vision. arXiv preprint arXiv:2311.14265 (2023)"},{"key":"2_CR69","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fang, Y., Cao, J., Zhang, Q., Wang, Z., Xu, R.: Masked spiking transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1761\u20131771 (2023)","DOI":"10.1109\/ICCV51070.2023.00169"},{"key":"2_CR70","doi-asserted-by":"crossref","unstructured":"Wu, H., Yang, Y., Chen, H., Ren, J., Zhu, L.: Mask-guided progressive network for joint raindrop and rain streak removal in videos. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7216\u20137225 (2023)","DOI":"10.1145\/3581783.3612001"},{"key":"2_CR71","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wu, H., Aviles-Rivero, A.I., Zhang, Y., Qin, J., Zhu, L.: Genuine knowledge from practice: diffusion test-time adaptation for video adverse weather removal. arXiv preprint arXiv:2403.07684 (2024)","DOI":"10.1109\/CVPR52733.2024.02419"},{"key":"2_CR72","unstructured":"Yang, Z., et al.: DashNet: a hybrid artificial and spiking neural network for high-speed object tracking. arXiv preprint arXiv:1909.12942 (2019)"},{"key":"2_CR73","doi-asserted-by":"crossref","unstructured":"Yao, M., Gao, H., Zhao, G., Wang, D., Lin, Y., Yang, Z., Li, G.: Temporal-wise attention spiking neural networks for event streams classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10221\u201310230 (2021)","DOI":"10.1109\/ICCV48922.2021.01006"},{"key":"2_CR74","unstructured":"Yao, M., Hu, J., Zhou, Z., Yuan, L., Tian, Y., Xu, B., Li, G.: Spike-driven transformer. arXiv preprint arXiv:2307.01694 (2023)"},{"issue":"8","key":"2_CR75","doi-asserted-by":"publisher","first-page":"9393","DOI":"10.1109\/TPAMI.2023.3241201","volume":"45","author":"M Yao","year":"2023","unstructured":"Yao, M., et al.: Attention spiking neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 9393\u20139410 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR76","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neucom.2022.11.046","volume":"520","author":"C Ye","year":"2023","unstructured":"Ye, C., Kornijcuk, V., Yoo, D., Kim, J., Jeong, D.S.: LaCERA: layer-centric event-routing architecture. Neurocomputing 520, 46\u201359 (2023)","journal-title":"Neurocomputing"},{"key":"2_CR77","doi-asserted-by":"publisher","unstructured":"Ye, T., Zhang, Y., Jiang, M., Chen, L., Liu, Y., Chen, S., Chen, E.: Perceiving and modeling density for image dehazing. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, pp. 130\u2013145. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19800-7_8","DOI":"10.1007\/978-3-031-19800-7_8"},{"key":"2_CR78","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Spiking transformers for event-based single object tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8801\u20138810 (2022)","DOI":"10.1109\/CVPR52688.2022.00860"},{"key":"2_CR79","doi-asserted-by":"crossref","unstructured":"Zheng, H., Wu, Y., Deng, L., Hu, Y., Li, G.: Going deeper with directly-trained larger spiking neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11062\u201311070 (2021)","DOI":"10.1609\/aaai.v35i12.17320"},{"key":"2_CR80","unstructured":"Zhou, C., et al.: Spikingformer: spike-driven residual learning for transformer-based spiking neural network. arXiv preprint arXiv:2304.11954 (2023)"},{"key":"2_CR81","unstructured":"Zhou, Z., et al.: Spikformer: when spiking neural network meets transformer. arXiv preprint arXiv:2209.15425 (2022)"},{"key":"2_CR82","unstructured":"Zhu, R.J., Wang, Z., Gilpin, L., Eshraghian, J.K.: Autonomous driving with spiking neural networks. arXiv preprint arXiv:2405.19687 (2024)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73116-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:17:08Z","timestamp":1730301428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73116-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031731150","9783031731167"],"references-count":82,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73116-7_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","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":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}