Comments (1)
- Yes, that right. Hard just means you apply an argmax to quantize the results. Specifically,
_output = model(rgb_0, anno_0, rgb_1, ref_index, i+1)
_output = F.interpolate(_output, (h,w), mode='bilinear')
# Hard
output = torch.argmax(_output, 1, keepdim=True).float()
# Soft
output = _output
-
Just do
x = image.float()[:,:,::4,::4]
because the center of CNN filters starts from the top left corner. -
About 59 (J&F Mean). Refer to table 5 of the paper.
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Related Issues (20)
- How to test my own video? HOT 1
- SpatialCorrelationSampler HOT 1
- In table 5,how many reference frames in only short? HOT 4
- what's the batchsize do you use for trainning multiple reference frames?
- test failed
- test problem HOT 1
- Where is the code for the Image Feature Alignment? HOT 2
- wrong usage of offset0 HOT 2
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- Question about Lab channel dropout HOT 2
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- hello, I am very interested in your work. However, I don't know how to visualize the output. result HOT 1
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