Contact Me:
✉ Email: [email protected]
⚡ Website: http://kunzhan.github.io/
⚡ Google Scholar: https://scholar.google.com.hk/citations?hl=en&user=sk7TcGAAAAAJ
ACM MM 2023: Improving semi-supervised semantic segmentation with dual-level Siamese structure network
Home Page: http://arxiv.org/abs/2307.13938
Contact Me:
✉ Email: [email protected]
⚡ Website: http://kunzhan.github.io/
⚡ Google Scholar: https://scholar.google.com.hk/citations?hl=en&user=sk7TcGAAAAAJ
Hi! May I ask how many GPUs and what kind of GPU are needed in your work? Thanks!
Hi! Have you done the ablation experiment of adopting mean-teacher to provide pseudo labels? In my opinion after various image and feature perturbations, it is no need to adopt MT, the model itself, with applying the classwise-aware threshold, can strong enough to dig out information and generate reliable pseudo labels. I think the performance boosting will be mild, am I right? Thanks!
Hi! Are the experimental results generated by the splits in U2PL?
Hi! Could you please provide the Pascal blender 1_2 split? I can only see 1_16, 1_8, 1_4 splits of Pascal blender dataset.
Hi! In this line of code, MSE Loss is calculated between pred_u_s1_norm and pred_u_s2_norm. However, since two cutmix boxes are different, img_u_s1 and img_u_s2 are definitely different. So how does it make sense to calculate the MSE Loss between two kinds of predictions? Thanks!
Hi! May I ask why you set two different folders of two dataset? I think they can share the same code under two dataset settings. Thanks!
I found your code is almost the same as unimatch, but you don't cite unimatch.
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