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[行爲識別] ICCV 2017 RPAN:An end-to-end recurrent pose-attention network for action recognition
時間 2020-12-24
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一、實驗 一篇引入關節點信息的論文,要點(abstrat中作者提到): 端到端的模型,lstm,沒有經過先提取關節特徵這種步驟; 不同於獨立的學習關節點特徵(human-joint features),這篇文章引入的pose-attention機制通過不同語義相關的關節點(semantically-related human joints)分享attention參數,然後將這些通過human-pa
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相關文章
1.
[行爲識別]RPAN:An end-to-end recurrent pose-attention network for action recognition
2.
[行爲識別]RPAN:An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos
3.
Temporal Pyramid Network for Action Recognition
4.
【CVPR2020】Temporal Pyramid Network for Action Recognition
5.
讀書筆記1:Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition
6.
視頻行爲識別[2]Temporal Segment Networks: Towards Good Practices for Deep Action Recognition[2016]
7.
[行爲識別] Two –Stream CNN for Action Recognition in Videos
8.
[行爲識別]VideoLSTM Convolves, Attends and Flows for Action Recognition
9.
【視頻行爲識別】3D Convolutional Neural Networks for Human Action Recognition:
10.
行爲識別 - Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
>>更多相關文章<<