태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
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).
Collects dense per-video-shot concept annotations. Source: Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach
Mr. HiSum is a large-scale video highlight detection and summarization dataset, which contains 31,892 videos selected from YouTube-8M dataset and reliable frame importance score labels aggregated from 50,000+ users per video.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.