【论文阅读笔记】Beyond Short Snippets: Deep Networks for Video Classification
主要目的： 视频分类 测试数据集： Sports-1M、UCF-101 方法概况： 使用在imageNet上预训练过的CNN（AlexNet或者GoogleLeNet）提取帧级特征，再将帧级特征和提取到的光流特征输入到池化框架或者LSTM进行训练，得到分类结果。 主要贡献： 1.提出采用CNN来得到视频级的全局描述，并且证明增大帧数能够显著提高分类性能。 2.通过在时序上共享参数，参数的数量在特征
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相关文章
- 1. [翻译]Beyond Short Snippets: Deep Networks for Video Classification
- 2. 读书笔记：Beyond Short Snippets: Deep Networks for Video Classification
- 3. Beyond Short Snippets: Deep Networks for Video Classification简介
- 4. 【论文阅读笔记】Large-scale Video Classification with Convolutional Neural Networks
- 5. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification阅读笔记
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