Summary - CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly

时间 2020-12-30

标签 summary 繁體版

原文原文链接

[paper] [code] 背景文中提出了一种用于识别高密度场景的网络模型 CSRNet，用于精确完成场景计数并生成高质量密度图像。CSRNet 由两部分构成：前半部分为卷积神经网络CNN，作为2D特征提取器，后半部分使用空洞卷积（Dilated Convolution）来增大感受野，并代替池化层。由于全卷积的结构，CSRNet很容易训练。文章在4个数据集上对CSRNet进行了测试，并取得了当

>>阅读原文<<

1. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—论文笔记
2. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
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. （翻译）Understanding Convolutional Neural Networks for NLP
7. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
8. Understanding and Visualizing Convolutional Neural Networks
9. 有效感受野--Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
10. 有效感觉野--Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
更多相关文章...
• Swift for 循环 - Swift 教程
• Scala for循环 - Scala教程
• 为了进字节跳动，我精选了29道Java经典算法题，带详细讲解
• Kotlin学习（一）基本语法

最新文章

1. 网络层协议以及Ping
2. ping检测
3. 为开发者总结了Android ADB 的常用十种命令
4. 3·15 CDN维权——看懂第三方性能测试指标
5. 基于 Dawn 进行多工程管理
6. 缺陷的分类
7. 阿里P8内部绝密分享：运维真经K8S+Docker指南”，越啃越香啊，宝贝
8. 本地iis部署mvc项目，问题与总结
9. InterService+粘性服务+音乐播放器
10. 把tomcat服务器配置为windows服务的方法

本站公众号

欢迎关注本站公众号,获取更多信息

1. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—论文笔记
2. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
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. （翻译）Understanding Convolutional Neural Networks for NLP
7. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
8. Understanding and Visualizing Convolutional Neural Networks
9. 有效感受野--Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
10. 有效感觉野--Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

>>更多相关文章<<