Comments (4)
Hi @qjadud1994,
There are two learnt parts in MCG:
- The low-level boundary detector, which is trained on BSDS500
- The Pareto front parameter search and the objectness ranking (a simple random forest), which are both trained on PASCAL VOC 2021 (train).
So in some sense it's "very little supervised" since it's "zero shot" on e.g. COCO, and the segmentation itself is not trained (heuristic on the hierarchy of regions given by the UCM)
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Thank you for your reply @jponttuset !
Then, in summary, "MCG requires object contour (boundary) labels for the training" is it right?
What I want to know is what kind of supervision sources are required in MCG.
from mcg.
Then, in summary, "MCG requires object contour (boundary) labels for the training" is it right?
Correct, but not on the target domain, just once completely class-agnosticly and then it transfers zero-shot to the datasets.
What I want to know is what kind of supervision sources are required in MCG.
I don't get this.
from mcg.
Thanks a lot @jponttuset !
Now, I understand about it.
It will be of great help to me.
from mcg.
Related Issues (20)
- Is there a python version of this ? HOT 1
- Strength of supervision required for training HOT 1
- Is there a C++ version of this code? HOT 1
- Dataset used for sf_modelFinal.mat HOT 4
- Saved MCG proposals for COCO HOT 1
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- A function couldn't be founded. HOT 2
- error:The needed function (mex_assess_one_sel) not found. Have you built the package properly? HOT 10
- Reference to non-existent field 'opts' HOT 1
- pretrained model 'sf_modelFinal' for PASCAL dataset HOT 1
- about the segmentation result in paper HOT 2
- error happened when i runing the pre-trained/demo_im2mcg.m HOT 2
- mkoctfile: unrecognized argument when running build.m in octave HOT 1
- matlab version HOT 1
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- How to obtain proposals shown in the paper? HOT 2
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