Please use this identifier to cite or link to this item: https://doi.org/10.1109/ACCESS.2019.2936604
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dc.titleTemporal Spiking Recurrent Neural Network for Action Recognition
dc.contributor.authorWang, W.
dc.contributor.authorHao, S.
dc.contributor.authorWei, Y.
dc.contributor.authorXiao, S.
dc.contributor.authorFeng, J.
dc.contributor.authorSebe, N.
dc.date.accessioned2022-01-11T06:21:16Z
dc.date.available2022-01-11T06:21:16Z
dc.date.issued2019
dc.identifier.citationWang, W., Hao, S., Wei, Y., Xiao, S., Feng, J., Sebe, N. (2019). Temporal Spiking Recurrent Neural Network for Action Recognition. IEEE Access 7 : 117165-117175. ScholarBank@NUS Repository. https://doi.org/10.1109/ACCESS.2019.2936604
dc.identifier.issn21693536
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/213752
dc.description.abstractIn this paper, we propose a novel temporal spiking recurrent neural network (TSRNN) to perform robust action recognition in videos. The proposed TSRNN employs a novel spiking architecture which utilizes the local discriminative features from high-confidence reliable frames as spiking signals. The conventional CNN-RNNs typically used for this problem treat all the frames equally important such that they are error-prone to noisy frames. The TSRNN solves this problem by employing a temporal pooling architecture which can help RNN select sparse and reliable frames and enhances its capability in modelling long-range temporal information. Besides, a message passing bridge is added between the spiking signals and the recurrent unit. In this way, the spiking signals can guide RNN to correct its long-term memory across multiple frames from contamination caused by noisy frames with distracting factors (e.g., occlusion, rapid scene transition). With these two novel components, TSRNN achieves competitive performance compared with the state-of-the-art CNN-RNN architectures on two large scale public benchmarks, UCF101 and HMDB51. © 2013 IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus OA2019
dc.subjectAction recognition
dc.subjectrecurrent neural network
dc.subjecttemporal spiking
dc.typeArticle
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1109/ACCESS.2019.2936604
dc.description.sourcetitleIEEE Access
dc.description.volume7
dc.description.page117165-117175
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