Comments (4)
Hello mate, have you solve the problem?
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@pandababyer Not yet
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@linjingxu I think the probs is the predicted importance scores for each frame, and the author want to select the key-shot which is the key segment. so in the function generate_summary, the author convert the key-shot select problem to a knapsack problem and find the key segment in 'picks'. I think that may explain why the score is around 0.5 rather than approach 0 or 1. Looking forward to your reply :)
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@pandababyer I have experimented with a couple of models that do the regression with sigmoid to output the frame importance score. Because the frame score is so sparse (at least this is true for summe dataset) the model learns to predict all 0, hence, the loss is pretty low. Very little gradient flow backwards. These regressive model tend to not have that much learning, according to my experience.
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Related Issues (20)
- Module Not Found Error HOT 15
- summary2video.py
- Question about Reward funtion ? HOT 1
- original Youtube and OVP datasets HOT 9
- How to find Reward Diversity score ?
- 0.0% Fscore for videos and probably wrong Summary. HOT 1
- Can not download the dataset.
- Could u tell me the relationship between 'gtscore' and 'gtsummary'? HOT 1
- Dataset for video summaization HOT 26
- Link to download dataset in repo is dead. HOT 2
- H5 dataset generation
- frame score
- Few Frame Ids from MachineSummary not available in VideoFrames generated. HOT 1
- Processed features and original videos HOT 1
- Problem with video index when running "summary2video.py" HOT 1
- GoogLe Net implementation HOT 5
- splits
- dist_mat = dist_mat.min(1, keepdim=True)[0] IndexError: Dimension out of range HOT 1
- DataSet Out of Date
- FileNotFoundError: [Errno 2] No such file or directory: 'path_to_your_model.pth.tar' HOT 1
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