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aimerykong / Recurrent-Pixel-Embedding-for-Instance-Grouping

CVPR2018 - pixel embedding & grouping for structured prediction, e.g., instance segmentation
https://www.cs.cmu.edu/~shuk/SMMMSG.html
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about the loss #5

Open JoyHuYY1412 opened 5 years ago

JoyHuYY1412 commented 5 years ago

Thank you for your work. I am not very familiar with MATLAB coding, but according to your paper, I think the loss you used for instance segmentation is: the margin loss after every step of mean-shift from 0 to T, and each time step t you use the updated data to get the loss lt Also, since the mean-shift kernel is set (according to your paper), all you train is the embedding from pixel to the 64-dimension feature. Am I right?

aimerykong commented 5 years ago

Yes. You can think in this way. The mean-shift behaves more like a loss rather than parametric module. But here are two things to notice --

  1. In the toy dataset (mnist), I set T as large as 5, but in practice, I use T=1 because the GPU memory is not enough to hold multiple copies of the transformed data. Of course this can be improved in different ways but I didn't push this in this paper.
  2. My remark (1) also indicates a different thing from what you say about the mean-shift kernel. Actually, the kernel is computed in each loop based on the new data, not the original 64-dim raw features.

Hope this helps. Thanks for your interest.

On Sun, Dec 2, 2018 at 1:46 AM JoyHuYY1412 notifications@github.com wrote:

Thank you for your work. I am not very familiar with MATLAB coding, but according to your paper, I think the loss you used for instance segmentation is: the margin loss after every step of mean-shift from 0 to T, and each time step t you use the updated data to get the loss lt Also, since the mean-shift kernel is set (according to your paper), all you train is the embedding from pixel to the 64-dimension feature. Am I right?

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JoyHuYY1412 commented 5 years ago

Yes, thank you for your help. I meant that the kernel's form is fixed since you choose a fixed bandwidth, while the data is updated each mean-shift step.