Visual temporal attention is a special case of visual attention that involves directing attention to specific instant of time. Similar to its spatial counterpart visual spatial attention, these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation[3] of deep learning models.
Video frames of the Parallel Bars action category in the UCF-101 dataset[1] (a) The highest ranking four frames in video temporal attention weights, in which the athlete is performing on the parallel bars; (b) The lowest ranking four frames in video temporal attention weights, in which the athlete is standing on the ground. All weights are predicted by the ATW CNN algorithm.[2] The highly weighted video frames generally captures the most distinctive movements relevant to the action category.
As visual spatial attention mechanism allows human and/or computer vision systems to focus more on semantically more substantial regions in space, visual temporal attention modules enable machine learning algorithms to emphasize more on critical video frames in video analytics tasks, such as human action recognition. In convolutional neural network-based systems, the prioritization introduced by the attention mechanism is regularly implemented as a linear weighting layer with parameters determined by labeled training data.[3]
ATW CNN architecture.[4] Three CNN streams are used to process spatial RGB images, temporal optical flow images, and temporal warped optical flow images, respectively. An attention model is employed to assign temporal weights between snippets for each stream/modality. Weighted sum is used to fuse predictions from the three streams/modalities.
Recent video segmentation algorithms often exploits both spatial and temporal attention mechanisms.[2][4] Research in human action recognition has accelerated significantly since the introduction of powerful tools such as Convolutional Neural Networks (CNNs). However, effective methods for incorporation of temporal information into CNNs are still being actively explored. Motivated by the popular recurrent attention models in natural language processing, the Attention-aware Temporal Weighted CNN (ATW CNN) is proposed[4] in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is implemented as temporal weighting and it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Experimental results show that the ATW CNN attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.
Literatureedit
Seibold VC, Balke J and Rolke B (2023): Temporal attention. Front. Cognit. 2:1168320. doi: 10.3389/fcogn.2023.1168320.
^ abZang, Jinliang; Wang, Le; Liu, Ziyi; Zhang, Qilin; Hua, Gang; Zheng, Nanning (2018). "Attention-Based Temporal Weighted Convolutional Neural Network for Action Recognition". IFIP Advances in Information and Communication Technology. Cham: Springer International Publishing. pp. 97–108. arXiv:1803.07179. doi:10.1007/978-3-319-92007-8_9. ISBN978-3-319-92006-1. ISSN 1868-4238. S2CID 4058889.
^ ab"NIPS 2017". Interpretable ML Symposium. 2017-10-20. Retrieved 2018-09-12.
^ abcWang, Le; Zang, Jinliang; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (2018-06-21). "Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network" (PDF). Sensors. 18 (7). MDPI AG: 1979. Bibcode:2018Senso..18.1979W. doi:10.3390/s18071979. ISSN 1424-8220. PMC6069475. PMID 29933555. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
April 11, 2024
visual, temporal, attention, special, case, visual, attention, that, involves, directing, attention, specific, instant, time, similar, spatial, counterpart, visual, spatial, attention, these, attention, modules, have, been, widely, implemented, video, analytic. Visual temporal attention is a special case of visual attention that involves directing attention to specific instant of time Similar to its spatial counterpart visual spatial attention these attention modules have been widely implemented in video analytics in computer vision to provide enhanced performance and human interpretable explanation 3 of deep learning models Video frames of the Parallel Bars action category in the UCF 101 dataset 1 a The highest ranking four frames in video temporal attention weights in which the athlete is performing on the parallel bars b The lowest ranking four frames in video temporal attention weights in which the athlete is standing on the ground All weights are predicted by the ATW CNN algorithm 2 The highly weighted video frames generally captures the most distinctive movements relevant to the action category As visual spatial attention mechanism allows human and or computer vision systems to focus more on semantically more substantial regions in space visual temporal attention modules enable machine learning algorithms to emphasize more on critical video frames in video analytics tasks such as human action recognition In convolutional neural network based systems the prioritization introduced by the attention mechanism is regularly implemented as a linear weighting layer with parameters determined by labeled training data 3 Contents 1 Application in Action Recognition 2 Literature 3 See also 4 ReferencesApplication in Action Recognition edit nbsp ATW CNN architecture 4 Three CNN streams are used to process spatial RGB images temporal optical flow images and temporal warped optical flow images respectively An attention model is employed to assign temporal weights between snippets for each stream modality Weighted sum is used to fuse predictions from the three streams modalities Recent video segmentation algorithms often exploits both spatial and temporal attention mechanisms 2 4 Research in human action recognition has accelerated significantly since the introduction of powerful tools such as Convolutional Neural Networks CNNs However effective methods for incorporation of temporal information into CNNs are still being actively explored Motivated by the popular recurrent attention models in natural language processing the Attention aware Temporal Weighted CNN ATW CNN is proposed 4 in videos which embeds a visual attention model into a temporal weighted multi stream CNN This attention model is implemented as temporal weighting and it effectively boosts the recognition performance of video representations Besides each stream in the proposed ATW CNN framework is capable of end to end training with both network parameters and temporal weights optimized by stochastic gradient descent SGD with back propagation Experimental results show that the ATW CNN attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments Literature editSeibold VC Balke J and Rolke B 2023 Temporal attention Front Cognit 2 1168320 doi 10 3389 fcogn 2023 1168320 See also editAttention Visual spatial attention Action Recognition Video content analysis Convolutional neural network Computer visionReferences edit Center UCF 2013 10 17 UCF101 Action Recognition Data Set CRCV Retrieved 2018 09 12 a b Zang Jinliang Wang Le Liu Ziyi Zhang Qilin Hua Gang Zheng Nanning 2018 Attention Based Temporal Weighted Convolutional Neural Network for Action Recognition IFIP Advances in Information and Communication Technology Cham Springer International Publishing pp 97 108 arXiv 1803 07179 doi 10 1007 978 3 319 92007 8 9 ISBN 978 3 319 92006 1 ISSN 1868 4238 S2CID 4058889 a b NIPS 2017 Interpretable ML Symposium 2017 10 20 Retrieved 2018 09 12 a b c Wang Le Zang Jinliang Zhang Qilin Niu Zhenxing Hua Gang Zheng Nanning 2018 06 21 Action Recognition by an Attention Aware Temporal Weighted Convolutional Neural Network PDF Sensors 18 7 MDPI AG 1979 Bibcode 2018Senso 18 1979W doi 10 3390 s18071979 ISSN 1424 8220 PMC 6069475 PMID 29933555 nbsp Material was copied from this source which is available under a Creative Commons Attribution 4 0 International License Retrieved from https en wikipedia org w index php title Visual temporal attention amp oldid 1159108476, wikipedia, wiki, book, books, library,
