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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

Papers

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Showing 21–28 of 28 papers

TitleStatusHype
A Stacking Ensemble Approach for Supervised Video Summarization—0
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness—0
Use of Affective Visual Information for Summarization of Human-Centric Videos—0
FullTransNet: Full Transformer with Local-Global Attention for Video Summarization—0
Diverse Sequential Subset Selection for Supervised Video Summarization—0
Video Summarization with Attention-Based Encoder-Decoder Networks—0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization—0
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