<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[paranoid_CNN的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/paranoid_CNN</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; paranoid_CNN]]></copyright><item><title><![CDATA[发个帖子，赚个积分可好？]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/89398998</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/89398998</guid><author>paranoid_CNN</author><pubDate>Fri, 19 Apr 2019 14:07:28 +0800</pubDate><description><![CDATA[真的不好意思了。

]]></description><category></category></item><item><title><![CDATA[Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/80766457</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/80766457</guid><author>paranoid_CNN</author><pubDate>Thu, 21 Jun 2018 21:25:05 +0800</pubDate><description><![CDATA[这篇文章可以说很晦涩了，作者用图卷积学习骨架数据，大概看一遍文章就知道作者建立了3-D Graph，然后用图卷积来学习所建立的图数据特征，然后再识别行为。但是这不是晦涩的地方，毕竟一个图，就看明白了，晦涩的地方是文章中的数据是的维度以及这个维度计算是怎么在图卷积中流动的。不类比传统卷积还好，偏偏现在大多都是拿传统卷积来讲图卷积，然后，会不自觉的用二维欧几里德图形里的传统卷积来考虑图卷积，这就开始蛋...]]></description><category></category></item><item><title><![CDATA[先挖坑：如何使用caffe在网络里用自定义的初始化参数。]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/79985931</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/79985931</guid><author>paranoid_CNN</author><pubDate>Wed, 18 Apr 2018 10:59:31 +0800</pubDate><description><![CDATA[。。。。想起来在埋吧。]]></description><category></category></item><item><title><![CDATA[CUDA编程]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/79730793</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/79730793</guid><author>paranoid_CNN</author><pubDate>Wed, 28 Mar 2018 17:17:27 +0800</pubDate><description><![CDATA[1. 什么是CUDA？ 
    CUDA全称是Compute Unified Device Architecture，中文名称即统一计算设备架构，它是NVIDIA公司提出了一种通用的并行计算平台和编程模型。使用CUDA，我们可以开发出同时在CPU和GPU上运行的通用计算程序，更加高效地利用现有硬件进行计算。 
    并行编程的中心思想是分而治之：一个问题可以首先被粗粒度地划分为若干较小的子问题...]]></description><category></category></item><item><title><![CDATA[八、tensorflow之结果可视化]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78332353</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78332353</guid><author>paranoid_CNN</author><pubDate>Tue, 24 Oct 2017 17:45:50 +0800</pubDate><description><![CDATA[import tensorflow as tf
import numpy as np
def add_layer(inputs, input_size, out_size, activation_function == None):
  Weights = tf.Variable(tf.random_normal([input_size,   
  out_size]))
  biases = tf]]></description><category></category></item><item><title><![CDATA[七、tensorflow之构建网络。]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78323967</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78323967</guid><author>paranoid_CNN</author><pubDate>Mon, 23 Oct 2017 21:59:13 +0800</pubDate><description><![CDATA[在前面的学习中，我们学会了如何通过tensorflow添加新层，具体代码如下：import tensorflow as tf
import numpy as np
def add_layer(inputs, input_size, out_size, activation_function == None):
  Weights = tf.Variable(tf.random_normal([inp]]></description><category></category></item><item><title><![CDATA[六、tensorflow之添加层。]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78289324</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78289324</guid><author>paranoid_CNN</author><pubDate>Thu, 19 Oct 2017 20:54:10 +0800</pubDate><description><![CDATA[对于tensorflow来讲，利用python添加层是通过定义函数定义的。 
比如，定一个层，实现activation(x*w+b) , activation是激活函数。import tensorflow as tf
def add_layer(inputs, in_size, out_size, activation=None):
  Weights = tf.Variable(tf.random]]></description><category></category></item><item><title><![CDATA[五、tensorflow之传入值（placeholder）]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78278167</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78278167</guid><author>paranoid_CNN</author><pubDate>Wed, 18 Oct 2017 21:30:33 +0800</pubDate><description><![CDATA[placeholder，顾名思义，就是一个储存东西的地方，这样的定义有点像变量variable，但他和变量完全不一样，因为每次要用，必须要和搭配字典传入新值后才能激活，不需要初始化。 
下面代码例子： 
计算两个数相乘import tensorflow as tf
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float]]></description><category></category></item><item><title><![CDATA[四、tensorflow之variable（变量）]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78278070</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78278070</guid><author>paranoid_CNN</author><pubDate>Wed, 18 Oct 2017 21:22:22 +0800</pubDate><description><![CDATA[还是通过学习教程中的例子来备忘一下： 
写一个变量，每次变量加1。import tensorflow as tf
state = tf.Variable(0, name='baba')
one = tf.constant(1)new_value = tf.add(state, one)
udpate = tf.assign(state, new_value)接下来一行代码非常重要，因为上述代码我们定]]></description><category></category></item><item><title><![CDATA[三、tensorflow之session]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78277927</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78277927</guid><author>paranoid_CNN</author><pubDate>Wed, 18 Oct 2017 21:13:31 +0800</pubDate><description><![CDATA[tensorflow中的会话功能（session）用来激活tensorflow结构中的变量，所有的变量要访问都要先通过session激活。 
一个例子： 
两个矩阵的内积：#定义两个矩阵
#coding=utf-8
import tensorflow as tf
matrix1 = tf.constant([[2,2]])
matrix2 = tf.constant([[3],]]></description><category></category></item><item><title><![CDATA[二、tensorflow线性拟合实例。]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78275678</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78275678</guid><author>paranoid_CNN</author><pubDate>Wed, 18 Oct 2017 17:47:45 +0800</pubDate><description><![CDATA[目的，给定一堆（本博客为100个）离散的点，通过单层的神经网络进行学习，工具tensorflow，下面开始具体教程：首先创造数据：import tensorflow as tf
import nump as np
**create data**
x_data = np.random.randn(100).astype(float32)
y_data = x_data*0.3 + 0.7
这个数据是我]]></description><category></category></item><item><title><![CDATA[一、ubuntu下tensorflow的安装。]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78265210</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/78265210</guid><author>paranoid_CNN</author><pubDate>Tue, 17 Oct 2017 19:40:23 +0800</pubDate><description><![CDATA[第一步：首先看一下电脑里是否已装python，版本是什么。命令分别为：whereis python和直接在终端输入python命令，看显示是2.7版本还是3.x版本。第二步： 
输入以下指令：对于2.7版本
$ sudo apt-get install python-pip python-dev
对于3.x版本
$sudo apt-get install python3-pip python3-de]]></description><category></category></item><item><title><![CDATA[Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/77933316</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/77933316</guid><author>paranoid_CNN</author><pubDate>Mon, 11 Sep 2017 15:04:55 +0800</pubDate><description><![CDATA[时光荏苒，岁月如梭，恍惚间，一道白光闪现，空中一尊大佛低声喃语：小伙子，你，是不是太久没读论文了！于是，我开始补论文。简略记下，防止忘掉。
本文是deepmind出品，目的，就一个，放出个关于视频方面的训练集kinetics，一个四百个类，每个类有至少四百个clips，每个clips十秒钟，属于从youtube上剪切的视频，然后对比了几种现在存在的用于行为识别的几种框架，具体如下图： 
其中，a，b]]></description><category></category></item><item><title><![CDATA[Spatiotemporal Multiplier Networks for Video Action Recognition]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/77866686</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/77866686</guid><author>paranoid_CNN</author><pubDate>Wed, 06 Sep 2017 15:08:30 +0800</pubDate><description><![CDATA[2017年cvpr，刚放出来的文章，大概看了一下，是在上一篇文章的扩展，上一篇文章见：http://blog.youkuaiyun.com/paranoid_cnn/article/details/77838574 问题：基于二流网络，本篇文章和给出上述链接的文章的思想是：原始的二流网络分别学习appearance stream和motion stream，虽然取得了比较work的性能，但时间和空间是分开学习的]]></description><category></category></item><item><title><![CDATA[Spatiotemporal Residual Networks for Video Action Recognition]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/77838574</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/77838574</guid><author>paranoid_CNN</author><pubDate>Mon, 04 Sep 2017 16:07:03 +0800</pubDate><description><![CDATA[这篇文章出自2016 NIPS， 作者是格林茨大学的Feichtenhofer。背景：几乎现在行为识别领域，比较work的工作都是基于二流网络。其中appearance和motion分别由两个不同的网络学习，并将两个网络的结果做融合后产生识别。本文创新部分： 
a、将二维空间ResNet网络部分扩展到时间域。即原来的二维空间网络W*H*C 映射到W*H*T*C， 
具体初始化方法如下：这样的好处有两]]></description><category></category></item><item><title><![CDATA[Caffe_Layer源码解析]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/64125122</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/64125122</guid><author>paranoid_CNN</author><pubDate>Mon, 20 Mar 2017 14:24:50 +0800</pubDate><description><![CDATA[include/caffe/layer]]></description><category></category></item><item><title><![CDATA[Action Recognition by Dense Trajectories]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53377544</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53377544</guid><author>paranoid_CNN</author><pubDate>Mon, 28 Nov 2016 14:46:55 +0800</pubDate><description><![CDATA[INTRODUCTION 
in general，trajectory features is extracted using the KLT tracker or matching SIFT descriptors between frames. 
however，extracted trajectores is various depend on  the methods， We sample]]></description><category></category></item><item><title><![CDATA[Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53312390</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53312390</guid><author>paranoid_CNN</author><pubDate>Wed, 23 Nov 2016 21:43:14 +0800</pubDate><description><![CDATA[Introduction此论文结合了hand-craft features 和 deep convolutional features两种提取特征的方式，方法名称：trajectory-pooled deepconvolutional descriptor ，简称TDD。 
整个过程分两步：
利用cnn学习convolutional features
采用trajectory-constrained]]></description><category></category></item><item><title><![CDATA[Two-Stream Convolutional Networks for Action Recognition in Videos]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53257031</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53257031</guid><author>paranoid_CNN</author><pubDate>Mon, 21 Nov 2016 10:38:59 +0800</pubDate><description><![CDATA[总概本篇文章提出了三点： 
1、propose一个two-stream卷积神经网络框架，时间流和空间流。 
2、表明提出的光流神经网络在action recognition上有较好的结果。 
3、multi-task learning，用于data argument。所有的训练和评估bendmark：UCF101和HMDB-51。设计成two-stream的原因：Video can naturall]]></description><category></category></item><item><title><![CDATA[caffe源码之VideoDataLayer]]></title><link>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53208109</link><guid>https://blog.youkuaiyun.com/paranoid_CNN/article/details/53208109</guid><author>paranoid_CNN</author><pubDate>Thu, 17 Nov 2016 22:55:51 +0800</pubDate><description><![CDATA[解析写这个文章是为了了解caffe的数据读取如果不是图片，而是视频，数据层是如何设计的。直观感觉对于caffe的入口层数据不是图像而是一帧帧视频的读取层，直观上的设计是好像没啥区别，因为视频本质也是一帧帧图像，但我真不知道怎么处理，可能最暴力的办法就是把一帧帧图像分别保存为单通道图像，然后将多幅单通道图像合并为一个多通道图像，那么一个视频本质上就是一幅多通道的图像，但问题就在于好像这样做不是很简单的]]></description><category></category></item></channel></rss>