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[CVPR'22] Semi-Supervised Video Semantic Segmentation with Inter-Frame Feature Reconstruction

License: MIT License

Python 63.64% Shell 0.21% C++ 17.56% Cuda 18.60%
cvpr2022 semantic-segmentation semi-supervised-learning semi-supervised-segmentation

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ifr's Issues

Can you give more details about the Table1. in paper?

Thanks for your wonderful work. From Table 1, I think it shows that the CAC or CPS method using the remaining unlabeled frames for training has a significant performance improvement compared to not using them. From 66.00 to 69.70, from 70.32 to 74.39. However, the paper says "no obvious improvement is gained.". Could you please explain that?
Snipaste_2022-10-08_18-00-51

About the definition of semi-supervised.

Some VSS methods aggregate features of neighborhood unlabeled frames to segment the current frame, so I think these methods also use unlabeled frames for training and they can be considered semi-supervised. Did I misunderstand something here?

Dose the test reuslts on several images rather than whole videos represent the performance of video semantic segmentation methods?

Here is another problem I'm confusing.
The task of video semantic segmentation is to segment each frame of videos. But only several frames are labeled in the test set, the test performance in experiments is on several images rather than whole videos. I think it can not represent the performance of video semantic segmentation methods. Did I misunderstand something here?

Access to pretrained model

Hi,

Thanks for your excellent work SSVS! I wonder if is it possible to provide the pretrained model on Cityscapes dataset?

Thank you so much!

Best,
Daisy

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