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[ICCV 2021 Oral] Mining Latent Classes for Few-shot Segmentation

Home Page: https://arxiv.org/abs/2103.15402

License: MIT License

Python 100.00%
few-shot-learning few-shot-segmentation iccv2021 semantic-segmentation

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miningfss's Issues

Offline Annotation and Mining Embedding

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.

pseudo loss and offline annotation

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.

Prototype Rectification

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 ?

Data replacement

Hello, I realized the effect of the paper. I changed the experimental data into my own data. Classification category: 1, 2, 3. The label image is as follows
QQ截图20220513153034
For my above classification, how to change fewshow Py code, the screenshot is as follows
21
Thank you for your guidance

offline Annotation

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。

About Trained Models

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.

About pretrained embedding network on offline annotation

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?

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