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Firstly, DRUNet is added as a filter to reduce the event noise during the conversion of the event stream into event tensor; secondly, a multi-scale convolutional layer is used instead of a single convolutional layer to extract feature information at different scales, and a depth-separable convolution is utilized to replace part of the standard convolution in the network structure to reduce the number of network parameters without losing the performance of the network; thirdly, multi-scale features are performed on different channel fusion and connecting the channel attention module to enhance the network\u2019s representation of effective features; then the classifier is redesigned to reduce feature loss and improve recognition accuracy by compressing the semantic information layer-by-layer and step-by-step; finally, the Adam optimizer based on the gradient centered algorithm is used for training to improve the network\u2019s generalization ability and training speed. On the N-Caltech101 and N-Cars datasets, the recognition accuracy of the model is 87.2\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mo>%<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    and 96.3\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mo>%<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , respectively, which is significantly higher than other algorithms.\n                  <\/jats:p>","DOI":"10.1007\/s11063-024-11551-x","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T04:02:22Z","timestamp":1708660942000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Event Target Recognition Based on DRUNet and Multi-scale Attention"],"prefix":"10.1007","volume":"56","author":[{"given":"Zi-Long","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"issue":"5","key":"11551_CR1","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/s19051005","volume":"19","author":"H-B Zhang","year":"2019","unstructured":"Zhang H-B, Zhang Y-X, Zhong B, Lei Q, Yang L, Du J-X, Chen D-S (2019) A comprehensive survey of vision-based human action recognition methods. 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