Efficient-AI-Backbones
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visualization in VIG
Hi, in the visualization part of VIG, I noticed that the neighbors are different for the same node in the 1st and the 12th block. Does this mean the adjacency matrix different between different blocks? As a beginner in GNN, this puzzles me. Looking for your reply, thanks!
Thanks for the attention. The graph is dynamically generated in grapher module. By default, we use MRConv in this repo: https://github.com/lightaime/deep_gcns_torch.
我想问一下,源码里DeepGCN类中:HW = 224 // 4 * 224// 4的4是不是每个patch的边长为4,然后有HW=3136个patch的意思?
是3136个patch,不过stem是几层卷积,每个patch之间有一定overlap,边长为7
真的十分感谢您的解答!!! 我看到有DownSample进行了下采样操作,具体是使用大小为3,步长为2,padding=1的卷积核进行完成的。 这样做在CNN层面上是2倍下采样。那么对于整个图来讲,节点数就减少了4倍。这是以一种什么方式进行节点数的减少呢?就好比GraphUNet是采用topK的方法进行图的下采样,那么DownSample对于节点数的减少又是什么原理呢?
Downsample这里没有太多特殊设计,就是参考CNN的
非常感谢您这段时间的答复! 请问您有了解过Slic超像素分割吗?对基于Slic超像素分割作为初始节点嵌入来做图像语义分割,有什么推荐的思路和方法吗?
Hi,
I was wondering if you could guide me to the code for visualization. I have been trying to find it but couldnt.
非常感谢您这段时间的答复! 请问您有了解过Slic超像素分割吗?对基于Slic超像素分割作为初始节点嵌入来做图像语义分割,有什么推荐的思路和方法吗?
你这个思路挺好的,建议继续深挖下去!