ACTION RECOGNITION: FIRST-AND SECOND-ORDER 3D FEATURE IN BI-DIRECTIONAL ATTENTION NETWORK
DC Field | Value | Language |
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dc.contributor.author | Kwon, Oh Chul | - |
dc.contributor.author | Kim, Junyeong | - |
dc.contributor.author | Yoo, Chang D. | - |
dc.date.accessioned | 2023-03-08T15:47:49Z | - |
dc.date.available | 2023-03-08T15:47:49Z | - |
dc.date.issued | 2018-10 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63833 | - |
dc.description.abstract | This paper considers a 3D convolutional neural network (CNN) that learns spatial and temporal regions of higher importance through a bi-direction long short-term memory (bi-LSTM) attention for action recognition. First- and second-order differences of spatially most relevant C3D features (sp-m-C3D) are obtained, and the concatenation of the two differences with the sp-m-C3D is used to generate a temporal attention on the sp-m-C3D using a bi-LSTM. Temporally most relevant sp-m-C3D features are fed into another bi-LSTM for action recognition. Essentially, the network learns spatial and temporal regions of high importance for action recognition. We evaluate the network on two public action recognition datasets: UCF-101 (YouTube Action) and HMDB51. The proposed network performs better compared to other state-of-the-art networks. | - |
dc.format.extent | 5 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | ACTION RECOGNITION: FIRST-AND SECOND-ORDER 3D FEATURE IN BI-DIRECTIONAL ATTENTION NETWORK | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICIP.2018.8451493 | - |
dc.identifier.bibliographicCitation | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), pp 3453 - 3457 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000455181503114 | - |
dc.identifier.scopusid | 2-s2.0-85062920663 | - |
dc.citation.endPage | 3457 | - |
dc.citation.startPage | 3453 | - |
dc.citation.title | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | C3D | - |
dc.subject.keywordAuthor | bi-directional LSTM | - |
dc.subject.keywordAuthor | Attention | - |
dc.subject.keywordAuthor | spatio-temporal bi-directional LSTM Attention | - |
dc.subject.keywordPlus | Attention | - |
dc.subject.keywordPlus | Bi-directional LSTM | - |
dc.subject.keywordPlus | C3D | - |
dc.subject.keywordPlus | Spatio-temporal bi-directional LSTM Attention | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | foreign | - |
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