태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Unsupervised video summarization approaches overcome the need for ground-truth data (whose production requires time-demanding and laborious manual annotation procedures), based on learning mechanisms that require only an adequately large collection of original videos for their training. Specifically, the training is based on heuristic rules, like the sparsity, the representativeness, and the diversity of the utilized input features/characteristics.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The SumMe dataset is a video summarization dataset consisting of 25 videos, each annotated with at least 15 human summaries (390 in total). Source: https://gyglim.github.io/me/vsum/index.html Image Source: https://gyglim.github.io/me/vsum/index.html
Introduced by Song et al. in TVSum: Summarizing web videos using titles. The TVSum dataset comprises 50 videos, with durations ranging from 1 to 11 minutes. These videos belong to 10 different categories associated with the TRECVid MED task, with 5 videos in each category, and were collected from YouTube. The video categories include various activities like changing a vehicle tire, making a sandwich, and flash mob gatherings. For annotation, each video was reviewed and rated by 20 users, who assigned frame-level importance scores on a scale from 1 (not important) to 5 (very important).
This dataset consists of 18 movies with duration range between 10 and 104 minutes leveraged from the OVSD dataset (Rotman et al., 2016). For these videos, the summary length limit is set to be the minimum between 4 minutes and 10% of the video length. Source: ILS-SUMM: ITERATED LOCAL SEARCH FOR UNSUPERVISED VIDEO SUMMARIZATION
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