Spatiotemporal Residual Networks for Video Action Recognition
这篇文章出自2016 NIPS， 作者是格林茨大学的Feichtenhofer。 背景：几乎现在行为识别领域，比较work的工作都是基于二流网络。其中appearance和motion分别由两个不同的网络学习，并将两个网络的结果做融合后产生识别。 本文创新部分： a、将二维空间ResNet网络部分扩展到时间域。即原来的二维空间网络W*H*C 映射到W*H*T*C， 具体初始化方法如下： 这样的好处
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相关文章
- 1. Spatiotemporal Multiplier Networks for Video Action Recognition
- 2. 论文阅读《Spatiotemporal Multiplier Networks for Video Action Recognition》
- 3. Improved Residual Networks for Image and Video Recognition
- 4. SlowFast Networks for Video Recognition
- 5. 论文笔记：SlowFast Networks for Video Recognition
- 6. Collaborative Spatiotemporal Feature Learning for Video Action Recognition 阅读笔记
- 7. PaperReading3-A Closer Look at Spatiotemporal Convolutions for Action Recognition
- 8. SlowFast Networks for Video Recognition ——译文
- 9. ResNet升级版（1）：IResNet | Improved Residual Networks for Image and Video Recognition
- 10. 【论文阅读】Improved Residual Networks for Image and Video Recognition (iResNet)