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JamesLiang819 / Instance_Unique_Querying

[NeurIPS 2022 Spotlight] Learning Equivariant Segmentation with Instance-Unique Querying
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When will you publish your code? #1

Open CQ-AI opened 2 years ago

CQ-AI commented 2 years ago

I read your paper, and very appreciated it! When will you publish the code on Github?

JamesLiang819 commented 2 years ago

Thank you for your interest. The core components of our method have been released and full code with detailed instructions will be released in two weeks.

CQ-AI commented 2 years ago

Thanks very much! But I cannot find your config.py. Where is it?

CQ-AI commented 2 years ago

Besides, I also cannot find the implementation of the proposed technique in the paper. Could you provide the path?

KiveeDong commented 2 years ago

Besides, I also cannot find the implementation of the proposed technique in the paper. Could you provide the path?

@CQ-AI solov2_contrast.py has the two proposed loss described in the paper.

CQ-AI commented 2 years ago

Besides, I also cannot find the implementation of the proposed technique in the paper. Could you provide the path?

@CQ-AI solov2_contrast.py has the two proposed loss described in the paper.

@KiveeDong Thanks! But I cannot retrieve this file. Could you provide the path?

KiveeDong commented 2 years ago

Besides, I also cannot find the implementation of the proposed technique in the paper. Could you provide the path?

@CQ-AI solov2_contrast.py has the two proposed loss described in the paper.

@KiveeDong Thanks! But I cannot retrieve this file. Could you provide the path?

@CQ-AI det/adet/modeling/solov2/solov2_contrast.py

CQ-AI commented 2 years ago

Besides, I also cannot find the implementation of the proposed technique in the paper. Could you provide the path?

@CQ-AI solov2_contrast.py has the two proposed loss described in the paper.

@KiveeDong Thanks! But I cannot retrieve this file. Could you provide the path?

@CQ-AI det/adet/modeling/solov2/solov2_contrast.py

@KiveeDong Thanks a lot! I always looked for the implementation code based on mmdetection, but could not find the proposed technique implementation described in the paper.