F1-score (Canonical): 67.5
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Supervised Video Summarization
Supervised video summarization rely on datasets with human-labeled ground-truth annotations (either in the form of video summaries, as in the case of the SumMe dataset, or in the form of frame-level importance scores, as in the case of the TVSum dataset), based on which they try to discover the underlying criterion for video frame/fragment selection and video summarization.
Source: Video Summarization Using Deep Neural Networks: A Survey
논문
F1-score (Canonical): 62.7
F1-score (Canonical): 63.4Kendall's Tau: 0.137Spearman's Rho: 0.165
F1-score (Canonical): 66.3F1-score (Augmented): 69.0Kendall's Tau: 0.108Spearman's Rho: 0.147
F1-score (Canonical): 63.4F1-score (Augmented): 64.2Kendall's Tau: 0.134Spearman's Rho: 0.163
F1-score (Canonical): 60.4Kendall's Tau: 0.181Spearman's Rho: 0.238
F1-score (Canonical): 61.4Kendall's Tau: 0.203Spearman's Rho: 0.267
F1-score (Canonical): 63.0F1-score (Augmented): 63.6Kendall's Tau: 0.162Spearman's Rho: 0.212
Kendall's Tau: 0.194Spearman's Rho: 0.255
F1-score (Canonical): 58.1F1-score (Augmented): 59.8
F1-score (Canonical): 58.5F1-score (Augmented): 57.1
F1-score (Canonical): 62.1F1-score (Augmented): 63.9
F1-score (Canonical): 60.1F1-score (Augmented): 60.3Kendall's Tau: 0.096Spearman's Rho: 0.107
F1-score (Canonical): 60.9F1-score (Augmented): 61.9Kendall's Tau: 0.097Spearman's Rho: 0.105