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[行为识别]RPAN:An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos
时间 2020-12-24
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这是一篇视频动作识别的论文,但值得注意的是,他利用了pose estimation的信息,即视频中人物的关节点的信息。论文没有在常见的HMDB和UCF101上测试,而是在两个带有关节点信息的小数据集上进行了测试, Sub-JHMDB and PennAction。 一、文章框架 1.卷积特征 本文首先用TSN提取每帧图片的feature map, 9×15×1024。即上图中的Ct,TSN并没有画
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相关文章
1.
[行为识别]RPAN:An end-to-end recurrent pose-attention network for action recognition
2.
[行为识别] ICCV 2017 RPAN:An end-to-end recurrent pose-attention network for action recognition
3.
[行为识别] Two –Stream CNN for Action Recognition in Videos
4.
Two-Stream SR-CNNs for Action Recognition in Videos
5.
译:Two-stream convolutional networks for action recognition in videos
6.
译:Two-Stream Convolutional Networks for Action Recognition in Videos
7.
视频动作识别--Two-Stream Convolutional Networks for Action Recognition in Videos
8.
Deep Learning for Videos: A 2018 Guide to Action Recognition
9.
动作识别阅读笔记(一)《Two-Stream Convolutional Networks for Action Recognition in Videos》
10.
视频行为识别[2]Temporal Segment Networks: Towards Good Practices for Deep Action Recognition[2016]
>>更多相关文章<<