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View Code? Open in Web Editor NEW[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation
Home Page: https://arxiv.org/abs/2103.15402
License: MIT License
[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation
Home Page: https://arxiv.org/abs/2103.15402
License: MIT License
I have a question in the offline annotation part.
It is mentioned that it used the pretrained embedding network, does the pretrained network mean the imagenet pretrained weight? If not, may I know how did you learn the model?
It's a great job. Will you publish coco trained model?
Hi Authors,
Thank you very much for your great work. Could you release the code of your method, rather than just the baseline?
Hello, thank you for your great contribution!
I have a question: trained model is pretrained backbone? It seems like each trained model which you provided is not the best model.
Thanks for sharing the code. i wonder where the code is about your offline annotation? and i do not see the pseudo loss during the training period.
Does this code contains any prototype rectification process mentioned in the paper ? I do not observe such moving average operation during episodic training. I think you share training without rectification. Is it true ?
I only saw the code of Loss_gt branch, but did not see the code about offline annotation operation.
Please supplement code as soon as possible。
Thanks for your perfect work!
The problem like the title, I want to know how to begin training the coco2014 dataset.
Hi,
Can I how to initialise the global background prototype P_{bg}^{global} in eqn. 6 in the paper? I didn't see it in the code. Thanks!
Hello, thank you for your great contribution!
I have a question: Can you provide the code of Offline Annotation and Mining Embedding? It seems like the repository doesn't include them.
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