CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
本文首先針對MCNN，提出了其兩個缺點：大量的訓練時間和無效的分支架構。 MCNN由於使用了多列網絡，參數比較多，需要訓練時間長容易理解。可是作者爲什麼說MCNN的多列是「無效的分支」呢？文中給出了實驗。 MCNN的主要設計目的是利用每一列的不同感受野來估計不同擁擠等級的場景。即設計者想讓三列網絡提取出不同的特徵。作者取出了Shanghai Part_A中的50個樣例，分別輸入到MCNN的三列網絡
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相關文章
- 1. Summary - CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly
- 2. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—論文筆記
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- 4. Understanding Convolutional Neural Networks for NLP
- 5. [轉] Understanding Convolutional Neural Networks for NLP
- 6. 人羣密度估計--Fully Convolutional Crowd Counting On Highly Congested Scenes
- 7. 經典論文閱讀--CSRNet
- 8. （翻譯）Understanding Convolutional Neural Networks for NLP
- 9. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
- 10. Understanding and Visualizing Convolutional Neural Networks