Margin MCC: Chance-Robust Metric for Video Boundary Detection with Allowed Margin
Author:
Affiliation:
1. ALBERT Inc., Tokyo, Japan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3613760
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3. Shixing Chen Xiaohan Nie David Fan Dongqing Zhang Vimal Bhat and Raffay Hamid. 2021. Shot Contrastive Self-Supervised Learning for Scene Boundary Detection. In CVPR. 9796--9805. Shixing Chen Xiaohan Nie David Fan Dongqing Zhang Vimal Bhat and Raffay Hamid. 2021. Shot Contrastive Self-Supervised Learning for Scene Boundary Detection. In CVPR. 9796--9805.
4. Davide Chicco and Giuseppe Jurman . 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics , Vol. 21 , 1 ( 2020 ), 1--13. Davide Chicco and Giuseppe Jurman. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, Vol. 21, 1 (2020), 1--13.
5. The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
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