Comments (1)
Hi: 你好:
I find the model somehow separates the vgg for RGB and flow apart, while in the paper it seems that there are two vggs, and the RGB and flow sequence go through them and are concated together in a 4096 * 25 matrix. Does that means that the following MLP and classifier only take the input of either RGB or flow into account?我发现该模型以某种方式将 RGB 和流的 vgg 分开,而在论文中似乎有两个 vgg,RGB 和流序列经过它们并在 4096 * 25 矩阵中连接在一起。这是否意味着下面的 MLP 和分类器仅考虑 RGB 或流的输入?
Thanks! 谢谢!
朋友你好,冒昧打扰您了,不知您是否有空想请教您一个问题。我最近再看这个项目的代码(因为tf的版本有点老,没法下载),有点困惑,根据代码我看到的是,将[b,w,h,c]的图片(不知图片是否有序且来自同一视频)输入到vgg16,vgg16的fc6层输出:vgg.fc6是[b,4096],经reshape后得到vgg_output:[b, 1, 4096],在后边他写道 initial_state = vgg_output[:,0,:] recurrence_input = vgg_output[:,1:,:] 这个就不对呀!!! 但是按照下面代码的理解vgg_output是3维的[b, 图片的数量,4096](4096是一张图片的特征),后面每次取一个批次里的一张图片的特征去做池化。 我没搞明白,取一个视频的多张图片提特征是在哪体现的,是batch来体现吗?
若有空希望大佬能帮忙看一下,十分感谢!
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