SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning

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Deep Learning SPCL
Overview

SPCL

SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive Learning

Update on 2021/11/25: ArXiv Version of SPCL is available at this https URL.

Code is coming soon.

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Comments
  • Question about paper

    Question about paper

    Hi, thanks for your awesome work on contrastive learning in UDA.

    I have some questions about the T-SNE visualization in Figure 4 as follows:

    1. It seems that the T-SNE visualization results are about pixels in the shown image but not the whole target domain dataset, is my understanding correct?
    2. How can I apply the T-SNE visualization for the whole target domain dataset? Is it feasible to use feature prototype of each category in each target image?
    3. Can you provide the code about T-SNE visualization, it's very grateful. :-)
    opened by super233 6
  • about warmup.py

    about warmup.py

    Thank you for sharing your code! I would like to ask why the warm-up phase uses adversarial learning? Isn't fully supervised learning of source domain images used for the warm-up? Using the target domain for adversarial learning would introduce noise into the selection of the source domain prototype, wouldn't it!

    opened by liwei1101 4
  • Some question about temperature parameter

    Some question about temperature parameter

    Very interesting work. But I want to ask a question, temperature is an important hyperparameter in contrastive learning, I found that in SDCA work, the temperature is set to 100, I want to know if the temperature in SPCL is the same as the SDCA setting, and if there is anything about the difference Temperatures lead to related experiments with different results.

    Looking forward to your reply.

    Best Regards, Qianmo

    opened by QianMo-wangxiaoyu 2
Owner
Binhui Xie (谢斌辉)
Binhui Xie (谢斌辉)
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