Comments (2)
From what I have observed I think the author was trying to train the first network to generate good optical flows. The second network adds small adjustments to this flow along with visibility maps. The visibility maps enforce the second network to ignore areas with less confidence from the warped images (g_I0_F_t_0_f and g_I0_F_t_0_f). This also causes the flow in those areas to be ignored as they are inconsequential to the final result (as those pixels are masked). This is just how I understand it and the actual reason might be something like this works better.
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Thanks for your reply and I think your understanding is reasonable.
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Related Issues (20)
- ffmpeg error while running evaluation code. HOT 1
- Output interpolated image frames are of different size then input frames?
- What is the function of transforms.Normalize(mean=negmean, std=std)? Why use a negative mean๏ผ
- Error converting file:D:\Coding\JAVASCRIPT\recless\out\4fpsRecording.mkv. Exiting.
- PackagesNotFoundError: torchvision-cpu==0.2.0 HOT 3
- Runtime Error (Traceback Attached) HOT 23
- Frames are not extracted for any input video
- Differences with butterflow
- Slowdown a point of the video HOT 1
- It does not seem to work for scale value below 4 HOT 1
- "index 1 is out of bounds for dimension 0 with size 1" HOT 2
- Reference frames are different from the original ones. HOT 1
- sh: /Users/*/Downloads/ffmpeg/ffmpeg: No such file or directory HOT 2
- Add local file to Google Colab?? Or change length??
- How can I find the dataset of 240-fps video from YouTube HOT 1
- Training
- Objective metrics
- Could be faster by not saving PNG frames
- Image edge flicker
- low rate of utilization in test video
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