{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:57:44Z","timestamp":1785488264686,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"DOI":"10.1145\/3774521.3774540","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:34:24Z","timestamp":1785483264000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Spiking Neural Networks with Evidential Deep Learning for Object Classification on Event Based Dataset"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4028-7950","authenticated-orcid":false,"given":"Prabhat","family":"Kumar","sequence":"first","affiliation":[{"name":"Indian Institute of Technology, Jammu, Jammu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4085-0168","authenticated-orcid":false,"given":"Himanshu","family":"Singh","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Jammu, Jammu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-0065","authenticated-orcid":false,"given":"Badri","family":"Narayan Subudhi","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Jammu, Jammu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7256-0897","authenticated-orcid":false,"given":"Geatanon Di","family":"Caterina","sequence":"additional","affiliation":[{"name":"University Of Strathclyde, Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8325-4908","authenticated-orcid":false,"given":"Vinit","family":"Jakhetiya","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Jammu, Jammu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9084-1847","authenticated-orcid":false,"given":"T.","family":"Veerakumar","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Goa, Veroda, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_3_3_1_2_2","first-page":"14927","volume-title":"Advances in Neural Information Processing Systems","author":"Amini Alexander","year":"2020","unstructured":"Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus. 2020. Deep Evidential Regression. In Advances in Neural Information Processing Systems , Vol.\u00a033. 14927\u201314937."},{"key":"e_1_3_3_1_3_2","first-page":"1","volume-title":"Advanced Seminar in Technical University of Munich","author":"Basegmez Erdem","year":"2014","unstructured":"Erdem Basegmez. 2014. The next generation neural networks: Deep learning and spiking neural networks. In Advanced Seminar in Technical University of Munich. Citeseer, 1\u201340."},{"key":"e_1_3_3_1_4_2","unstructured":"Andrew Brock Jeff Donahue and Karen Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1809.11096 (2018)."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.36"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Anthony\u00a0N. Burkitt. 2006. A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biological Cybernetics 95 (2006) 1\u201319.","DOI":"10.1007\/s00422-006-0068-6"},{"key":"e_1_3_3_1_7_2","unstructured":"Ekin\u00a0D Cubuk Barret Zoph Dandelion Mane Vijay Vasudevan and Quoc\u00a0V Le. 2018. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1805.09501 (2018)."},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"e_1_3_3_1_9_2","unstructured":"Lei Deng Yujie Wu Yifan Hu Lei Liang Wenyuan Ding Guoqi Li Han Li Yi Ding Yuan Xie and Luping Shi. 2020. Modeling biological brains with deep learning: A review of spiking neural networks. Frontiers in Neuroscience 14 (2020) 118."},{"key":"e_1_3_3_1_10_2","unstructured":"Shikuang Deng and Shi Gu. 2021. Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2103.00476 (2021)."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2016.7738691"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Wei Fang Yanqi Chen Jianhao Ding Zhaofei Yu Timoth\u00e9e Masquelier Ding Chen Liwei Huang Huihui Zhou Guoqi Li and Yonghong Tian. 2023. Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence. Science Advances 9 40 (2023) eadi1480.","DOI":"10.1126\/sciadv.adi1480"},{"key":"e_1_3_3_1_13_2","first-page":"10226","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Fang Wei","year":"2021","unstructured":"Wei Fang, Jianhao Yu, Yujie Chen, Xiaolin Huang, Yufei Zhou, Yonghong Tian, and Tim Chen. 2021. Deep residual learning in spiking neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol.\u00a035. 10226\u201310234."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00266"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Guillermo Gallego Tobi Delbr\u00fcck Garrick Orchard Chiara Bartolozzi Brian Taba Andrea Censi Stefan Leutenegger Andrew\u00a0J. Davison J\u00f6rg Conradt Kostas Daniilidis and Davide Scaramuzza. 2022. Event-based vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44 1 (2022) 154\u2013180.","DOI":"10.1109\/TPAMI.2020.3008413"},{"key":"e_1_3_3_1_16_2","unstructured":"Yaroslav Ganin Evgeniya Ustinova Hana Ajakan Pascal Germain Hugo Larochelle Fran\u00e7ois Laviolette Mario Marchand and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17 1 (2016) 2096\u20132030."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"e_1_3_3_1_18_2","unstructured":"Ishaan Gulrajani Faruk Ahmed Martin Arjovsky Vincent Dumoulin and Aaron\u00a0C Courville. 2017. Improved training of wasserstein gans. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Yifan Guo Xiaolin Huang and Zongyuan Ma. 2023. Direct learning-based deep spiking neural networks: a review. Frontiers in Neuroscience 17 (2023) 1209795.","DOI":"10.3389\/fnins.2023.1209795"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Yongcheng Jing Yezhou Yang Zunlei Feng Jingwen Ye Yizhou Yu and Mingli Song. 2019. Neural style transfer: A review. IEEE Transactions on Visualization and Computer Graphics 26 11 (2019) 3365\u20133385.","DOI":"10.1109\/TVCG.2019.2921336"},{"key":"e_1_3_3_1_21_2","unstructured":"Youngeun Kim and Priyadarshini Panda. 2021. Optimizing Deeper Spiking Neural Networks for Dynamic Vision Sensing. Neural Networks (2021)."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Youngeun Kim and Priyadarshini Panda. 2021. Revisiting batch normalization for training low-latency deep spiking neural networks from scratch. Frontiers in Neuroscience 15 (2021) 773954.","DOI":"10.3389\/fnins.2021.773954"},{"key":"e_1_3_3_1_23_2","unstructured":"Diederik\u00a0P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1412.6980 (2014)."},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Alexander Kugele Thomas Pfeil Michael Pfeiffer and Elisabetta Chicca. 2020. Efficient processing of spatio-temporal data streams with spiking neural networks. Frontiers in Neuroscience 14 (2020) 439.","DOI":"10.3389\/fnins.2020.00439"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"Xavier Lagorce Garrick Orchard Francesco Galluppi Bertram\u00a0E Shi and Ryad\u00a0B Benosman. 2016. Hots: a hierarchy of event-based time-surfaces for pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 7 (2016) 1346\u20131359.","DOI":"10.1109\/TPAMI.2016.2574707"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"Hongmin Li Hanchao Liu Xiangyang Ji Guoqi Li and Luping Shi. 2017. CIFAR10-DVS: An Event-Stream Dataset for Object Classification. Frontiers in Neuroscience 11 (2017) 309.","DOI":"10.3389\/fnins.2017.00309"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00437"},{"key":"e_1_3_3_1_28_2","unstructured":"Ilya Loshchilov and Frank Hutter. 2016. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1608.03983 (2016)."},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"Wolfgang Maass. 1997. Networks of spiking neurons: the third generation of neural network models. Neural Networks 10 9 (1997) 1659\u20131671.","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"crossref","unstructured":"Garrick Orchard Ajinkya Jayawant Gregory\u00a0K Cohen and Nitish Thakor. 2015. Converting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in Neuroscience 9 (2015) 437.","DOI":"10.3389\/fnins.2015.00437"},{"key":"e_1_3_3_1_31_2","unstructured":"Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga et\u00a0al. 2019. Pytorch: An imperative style high-performance deep learning library. Advances in neural information processing systems 32 (2019) 8026\u20138037."},{"key":"e_1_3_3_1_32_2","unstructured":"Bharath Ramesh Hong Yang Garrick Orchard Ngoc\u00a0Anh Le\u00a0Thi Shihao Zhang and Cheng Xiang. 2019. Dart: distribution aware retinal transform for event-based cameras. IEEE transactions on pattern analysis and machine intelligence 42 11 (2019) 2767\u20132780."},{"key":"e_1_3_3_1_33_2","unstructured":"Nitin Rathi and Kaushik Roy. 2020. DIET-SNN: Direct input encoding with leakage and threshold optimization in deep spiking neural networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2008.03658 (2020)."},{"key":"e_1_3_3_1_34_2","unstructured":"Nitin Rathi Gopalakrishnan Srinivasan Priyadarshini Panda and Kaushik Roy. 2020. Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2005.01807 (2020)."},{"key":"e_1_3_3_1_35_2","unstructured":"Henri Rebecq Daniel Gehrig Guillermo Gallego and Davide Scaramuzza. 2021. Event-based vision for autonomous driving: A paradigm shift for self-driving cars. IEEE Transactions on Pattern Analysis and Machine Intelligence 43 6 (2021) 1626\u20131640."},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSPA64953.2025.10933084"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Connor Shorten and Taghi\u00a0M Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of Big Data 6 1 (2019) 1\u201348.","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_3_1_39_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1409.1556 (2014)."},{"key":"e_1_3_3_1_40_2","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1409.1556 (2014)."},{"key":"e_1_3_3_1_41_2","first-page":"164","volume-title":"Proceedings of the International Conference on Pattern Recognition","author":"Singh Himanshu","year":"2024","unstructured":"Himanshu Singh, Avijit Dey, Badri\u00a0Narayan Subudhi, and Vinit Jakhetiya. 2024. Project and pool: An action localization network for localizing actions in untrimmed videos. In Proceedings of the International Conference on Pattern Recognition. 164\u2013178."},{"key":"e_1_3_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS65446.2025.11149948"},{"key":"e_1_3_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21662"},{"key":"e_1_3_3_1_44_2","doi-asserted-by":"crossref","unstructured":"Himanshu Singh Saurabh Suman Badri\u00a0Narayan Subudhi Vinit Jakhetiya and Ashish Ghosh. 2022. Action recognition in dark videos using spatio-temporal features and bidirectional encoder representations from transformers. IEEE Transactions on Artificial Intelligence 4 6 (2022) 1461\u20131471.","DOI":"10.1109\/TAI.2022.3221912"},{"key":"e_1_3_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00186"},{"key":"e_1_3_3_1_46_2","doi-asserted-by":"crossref","unstructured":"Amir Tavanaei Mohammad Ghodrati Saeed\u00a0Reza Kheradpisheh Timoth\u00e9e Masquelier and Anthony Maida. 2019. Deep learning in spiking neural networks. Neural Networks 111 (2019) 47\u201363.","DOI":"10.1016\/j.neunet.2018.12.002"},{"key":"e_1_3_3_1_47_2","unstructured":"Florian Tram\u00e8r Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh and Patrick McDaniel. 2017. Ensemble adversarial training: Attacks and defenses. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1705.07204 (2017)."},{"key":"e_1_3_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/INDICON63790.2024.10958378"},{"key":"e_1_3_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533738"},{"key":"e_1_3_3_1_50_2","doi-asserted-by":"crossref","unstructured":"Paul\u00a0J. Werbos. 1990. Backpropagation through time: what it does and how to do it. Proc. IEEE 78 10 (1990) 1550\u20131560.","DOI":"10.1109\/5.58337"},{"key":"e_1_3_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"e_1_3_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Zhenzhi Wu Hehui Zhang Yihan Lin Guoqi Li Meng Wang and Ye Tang. 2021. LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing. IEEE Transactions on Neural Networks and Learning Systems 33 11 (2021) 6249\u20136262.","DOI":"10.1109\/TNNLS.2021.3073016"},{"key":"e_1_3_3_1_53_2","doi-asserted-by":"crossref","unstructured":"Kazuyuki Yamazaki Vu-Khac Vo-Ho Dushyant Bulsara and Nam Le. 2022. Spiking neural networks and their applications: a review. Brain Sciences 12 7 (2022) 863.","DOI":"10.3390\/brainsci12070863"},{"key":"e_1_3_3_1_54_2","unstructured":"Zhewei Yan Zhijie Bai and Wai-Fong Wong. 2024. Reconsidering the energy efficiency of spiking neural networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2409.08290 (2024)."},{"key":"e_1_3_3_1_55_2","doi-asserted-by":"crossref","unstructured":"Bingbing Yin Federico Corradi and Sander\u00a0M. Boht\u00e9. 2021. Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. Nature Machine Intelligence 3 (2021) 905\u2013913.","DOI":"10.1038\/s42256-021-00397-w"},{"key":"e_1_3_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01540"},{"key":"e_1_3_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17320"},{"key":"e_1_3_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_34"}],"event":{"name":"ICVGIP 2025: Indian Conference on Computer Vision, Graphics, and Image Processing","location":"Mandi Himachal Pradesh India","acronym":"ICVGIP 2025"},"container-title":["Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774521.3774540","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:02:03Z","timestamp":1785484923000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774521.3774540"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":57,"alternative-id":["10.1145\/3774521.3774540","10.1145\/3774521"],"URL":"https:\/\/doi.org\/10.1145\/3774521.3774540","relation":{},"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"2026-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}