论文笔记--Appearance-and-Relation Networks for Video Classification
Appearance-and-Relation Networks for Video Classification是王利民组2017年底的发表的一篇工作。 主要工作是在3D卷积的基础上，提升了action recognition的准确率，而且没有像目前主流的two-stream方法那样使用使用光流信息，因为光流的提取速度特别慢，这是一个未来的研究趋势，更不会像IDT那套方法一样计算复杂。 虽然目前
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相关文章
- 1. 【论文阅读笔记】Beyond Short Snippets: Deep Networks for Video Classification
- 2. 论文笔记：SlowFast Networks for Video Recognition
- 3. SlowFast Networks for Video Recognition论文笔记
- 4. Appearance-and-Relation Networks for Video Classification
- 5. 【论文阅读笔记】Large-scale Video Classification with Convolutional Neural Networks
- 6. CV论文笔记（二） Large-scale Video Classification with Convolutional Neural Networks
- 7. [翻译]Beyond Short Snippets: Deep Networks for Video Classification
- 8. 读书笔记：Beyond Short Snippets: Deep Networks for Video Classification
- 9. Bag of Tricks for Image Classification with Convolutional Neural Networks论文笔记
- 10. 【论文阅读笔记】MULTI-SCALE DENSE NETWORKS FOR RESOURCE EFFICIENT IMAGE CLASSIFICATION