Comments (5)
Hi @lauraset
Thank you for your interest in our work, and I am happy that you learned from our work.
The state-of-the-art methods that I have used for the comparisons in my paper are not publically available. Hence, I have implemented those methods on my own and you can find all of those methods by shifting the branch
.
- s4GAN: change the branch from
main
tos4GAN
: https://github.com/wgcban/SemiCD/tree/s4GAN - SemiCDNet (TGRS-21): change the branch from
main
toSemiCDNet(TGRS21)
: https://github.com/wgcban/SemiCD/tree/SemiCDNet(TGRS21) - AdvNet: Change the branch from
main
toAdvNet
: https://github.com/wgcban/SemiCD/tree/AdvNet - RoCD: Change the branch from
main
toRoCD
: https://github.com/wgcban/SemiCD/tree/RoCD
I hope this answers your question. Please let me know if you have more questions!
Best,
Chaminda.
from semicd.
Hi, @wgcban . Thank you for your detailed response. You are really a nice person.
One issue confused me. That is the high similarity between your method with the CCT (cross-consistency training) method. Actually, I guess that the temporal consistency (e.g., the high similarity of unchanged areas and the high dissimilarity of changed areas between bi-temporal images) embedded in the change detection is not utilized yet.
I guess this point may differentiate the change detection task from the semantic segmentation one for the CCT method. Anyway, thank you for your help.
Best regards,
Yinxia Cao.
from semicd.
Yes, you are correct that the proposed method is somewhat similar to CCT; however, the main idea that we want to convey from this work is that the cluster assumption exists even for bitemporal images in the deep feature difference domain and consistency training can be used for semi-supervised (SS) change detection, while CCT considers SS semantic segmentation with RGB images, not bi-temporal images.
Yes, I agree with your point that our method does not integrate the time consistency of change and no-change regions, and I will address this in my future work. Thanks for your suggestion.
Best,
Chaminda.
from semicd.
@wgcban , Thank you for your nice help. I have no further questions.
from semicd.
Nice, I am closing this issue.
from semicd.
Related Issues (10)
- data preparation HOT 8
- Problems with custom dataset
- Downloading link of processed datasets HOT 2
- heatmap visualization HOT 1
- about the best_model.pth HOT 3
- about comparison experiment HOT 2
- Can you share the Comparison Experiments Pre-trained models HOT 2
- The train.py file could not be found HOT 4
- inference results are all zeros HOT 3
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