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VLAD Parameters #19
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Yes - NetVLAD is not a standard layer so it is not included in MatConvNet by default. It is a custom layer implemented as a class, so you are missing one of these two things (or both):
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So, where are the weights for the netvlad layer stored? I wish to access those. |
If you download the NetVLAD code and make sure it is in the path, loading the network from the mat file will work instead of producing some empty layers. Then you can examine the loaded NetVLAD layer and access it's weights. E.g. net.layers{31}.weights |
I run your demo code, Am correct in assuming that the bias is not trained in this case? |
Yes, layerVLAD.m code is without bias while layerVLADv2.m is with bias (we mentioned on arXiv appendix that we fix the bias, but it seems we accidentally dropped it from the v3 version of the paper). For the setting in the paper, there is not much difference between the two, and the reason is that input features are L2 normalized in which case you can do the assignment with a simple scalar product and don't need bias (e.g. see the assignment equation 2, expand it and assume |xi|=1 and |ck|=1, then you don't get any bias terms as they all cancel out). If they were not L2 normalized, the two are not similar and layerVLADv2 should probably be used (but this is just theory, I don't know if it is needed in practice). |
Ok, I get your point. How about the BatchNormalization? Is this network finetuned from the ImageNet VGG without batchnorm updates? |
As you can see from your list of layers - there are no batch norm layers in the original VGG network |
Hi,
I am trying to look at the VLAD parameters like w, b, c, ie. the centroid of the
cluster etc. in as in the NetVLAD paper.
I downloaded the best model:
Running this script gave:
So, I tried to look at layer 30 and 32, but I fail to see the learned weights.
I do see the weights for other layers though.
struct with fields:
Am I missing something?
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