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Implement algorithm similar to that described in Koala-36M. Add `KoalaDetector` and `detect-koala` command. #441
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| | HashDetector | 92.96 | 76.27 | 83.79 | 16.26 | | ||
| | HistogramDetector | 90.55 | 72.76 | 80.68 | 16.13 | | ||
| | ThresholdDetector | 0.00 | 0.00 | 0.00 | 18.95 | | ||
| | KoalaDetector | 86.83 | 78.38 | 82.39 | 97.75 | |
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Oh, interesting. Even though Koala detector uses ML-based methods, it does not achieve good performance compared with rule-based detectors?
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It's possible I have a bug somewhere in the implementation. This was a first-pass at implementing this with just a single video test case. I haven't reviewed the code in some time - if you spot anything that looks wrong, please let me know!
Unfortunately I haven't had time to work on this PR lately. If you want to develop this detector further, I would be happy to clean this PR up and get it ready for review.
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I see. To be honest, I am not sure about the performance of Koala-37M because the authors do not evaluate the shot detection accuracy in the paper. Because they released the code (https://github.com/KwaiVGI/Koala-36M/blob/main/trainsition_detect/VideoTransitionAnalyzer.py), let me check the performance on BBC and AutoShot. I will report it in this thread.
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Work in progress. #441
Based off of https://koala36m.github.io/ (will update this with proper citation when ready for review)