hbilen / WSDDN

Weakly Supervised Deep Detection Networks (CVPR 2016)
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Spatial regularizer #1

Closed vadimkantorov closed 7 years ago

vadimkantorov commented 8 years ago

Hi Hakan,

Thanks for putting the code online, it's really a nice and concise code base! I've got two questions concerning the spatial regularizer:

1) As far as I can see in https://github.com/hbilen/WSDDN/blob/master/layers/LossTopBoxSmoothProb.m#L69, you are also putting the probs values under the square opposite to the formulation in subsection 3.4 of the paper. Also the gradient computation in https://github.com/hbilen/WSDDN/blob/master/layers/LossTopBoxSmoothProb.m#L85 doesn't square the probs value that doesn't correspond to the forward pass code. Am I missing something or is it a bug?

2) Do I understand correctly that you differentiate the regularizer only wrt fc7 outputs?

Thanks in advance!

hbilen commented 7 years ago

I have updated the equation in the paper. Thanks to Vadim Kantorov and Siniša Šegvić. Now the implementation should be consistent with the paper.

senthilps8 commented 7 years ago

Sorry for reviving this. Can you point me to the new version of the paper? Correct me if I'm wrong. From what I understand, this and this version still have the issue that @vadimkantorov pointed out.

hbilen commented 7 years ago

Thanks Senthil, the paper was somehow not updated in the vgg website. I think it should be the right version now. Here is the link:

http://www.robots.ox.ac.uk/~vgg/publications/2016/Bilen16/bilen16.pdf

On 18/12/16 21:20, Senthil Purushwalkam wrote:

Sorry for reviving this. Can you point me to the new version of the paper? Correct me if I'm wrong. From what I understand, this https://www.robots.ox.ac.uk/%7Evgg/publications/2016/Bilen16/bilen16.pdf and this https://arxiv.org/pdf/1511.02853v3.pdf version still have the issue that @vadimkantorov https://github.com/vadimkantorov pointed out.

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vadimkantorov commented 7 years ago

Still, from what I can see, the forward pass squares the probs while the backward doesn't. Shouldn't they be consistent on this?

senthilps8 commented 7 years ago

Yes, I think the backward prop uses the previous formulation of the loss #4

hbilen commented 7 years ago

Thanks, you are right. I guess if we square probs term, it is gonna be correct, isn't it? This might require some change in the scaling term. I will try to run an experiment with that.

vadimkantorov commented 7 years ago

Yep, seems squaring probs in backward pass would make it correct

css1995 commented 5 years ago

@vadimkantorov Sorry, could you show me the code after squaring probs in backward pass?