Comments (4)
n your paper “Superpixel Sampling Networks”,I was confused by the loss function:
which symbol denote the output of the SSN?
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Q is the output of the network, which gives soft-mapping from pixels to superpixels. Q is of size Nx9 in this work, where N is number of pixels and 9 denotes the number of surrounding 3x3 superpixels. R can be any pixel representation that we want superpixels to preserve. R can be pixel RGB values, semantic segmentation, optical flow or some other pixel features.
from ssn_superpixels.
@varunjampani Thanks for your answer ! one more question. Does this SSN_network only predict the super-pixel in image, but not predict result of specific task like segmentation or optical flow?
from ssn_superpixels.
SSN network only predicts superpixels that are tuned to represent representations (such as segmentation, optical flow etc.). SSN does not predict results for those tasks.
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Related Issues (20)
- Aboutn
- where to install Caffe inside "ssn_superpixels" HOT 2
- About some custom caffe layer ? HOT 3
- input image size different for training and testing on the BSDS500 dataset?? HOT 1
- Some question about initialization of group conv layer 'concat_spixel_feat_50' HOT 3
- about Evaluation HOT 1
- Some questions on source code HOT 7
- what does the training loss curve look like HOT 5
- The implementation of pytorch version HOT 6
- I cannot understand “n*9” mentioned in the paper HOT 1
- F0630 15:37:53.939426 12256 math_functions.cu:79] Check failed: error == cudaSuccess (74 vs. 0) misaligned address *** Check failure stack trace: ***
- Why is random cropping / patching and random scaling applied continuously throughout training? HOT 1
- how to run the code??? HOT 1
- Question of the function of spix_init HOT 2
- about the label and loss HOT 2
- Superpixel border issue on BSDS500 dataset HOT 5
- Pytorch Implementation License
- custom training HOT 1
- extract the features of the superpixel area
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