F1-score (Canonical): 67.5
Papers with Code · CC BY-SA 4.0
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
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
데이터셋
2개논문
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): 62.1F1-score (Augmented): 63.9
F1-score (Canonical): 63.4F1-score (Augmented): 64.2Kendall's Tau: 0.134Spearman's Rho: 0.163
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
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.