CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
本文首先针对MCNN，提出了其两个缺点：大量的训练时间和无效的分支架构。 MCNN由于使用了多列网络，参数比较多，需要训练时间长容易理解。可是作者为什么说MCNN的多列是“无效的分支”呢？文中给出了实验。 MCNN的主要设计目的是利用每一列的不同感受野来估计不同拥挤等级的场景。即设计者想让三列网络提取出不同的特征。作者取出了Shanghai Part_A中的50个样例，分别输入到MCNN的三列网络
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相关文章
- 1. Summary - CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly
- 2. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—论文笔记
- 3. 人群密度估计--CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
- 4. Understanding Convolutional Neural Networks for NLP
- 5. [转] Understanding Convolutional Neural Networks for NLP
- 6. 人群密度估计--Fully Convolutional Crowd Counting On Highly Congested Scenes
- 7. 经典论文阅读--CSRNet
- 8. （翻译）Understanding Convolutional Neural Networks for NLP
- 9. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
- 10. Understanding and Visualizing Convolutional Neural Networks