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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 1–25 of 28 papers

TitleStatusHype
Combining Global and Local Attention with Positional Encoding for Video SummarizationCode1
DSNet: A Flexible Detect-to-Summarize Network for Video SummarizationCode1
Align and Attend: Multimodal Summarization with Dual Contrastive LossesCode1
Progressive Video Summarization via Multimodal Self-supervised LearningCode1
Video Joint Modelling Based on Hierarchical Transformer for Co-summarizationCode1
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
Self-Attention Recurrent Summarization Network with Reinforcement Learning for Video Summarization TaskCode1
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization—0
Improving Sequential Determinantal Point Processes for Supervised Video Summarization—0
Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video—0
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
Joint Video Summarization and Moment Localization by Cross-Task Sample Transfer—0
A Stacking Ensemble Approach for Supervised Video Summarization—0
CSTA: CNN-based Spatiotemporal Attention for Video Summarization—0
Diverse Sequential Subset Selection for Supervised Video Summarization—0
FullTransNet: Full Transformer with Local-Global Attention for Video Summarization—0
Hierarchical Multimodal Transformer to Summarize Videos—0
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic 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
Video Summarization with Attention-Based Encoder-Decoder Networks—0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness RewardCode0
Discriminative Feature Learning for Unsupervised Video SummarizationCode0
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