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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 1–28 of 28 papers

TitleStatusHype
Progressive Video Summarization via Multimodal Self-supervised LearningCode1
DSNet: A Flexible Detect-to-Summarize Network for Video SummarizationCode1
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
Combining Global and Local Attention with Positional Encoding for Video SummarizationCode1
Align and Attend: Multimodal Summarization with Dual Contrastive LossesCode1
Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web PriorCode0
CLIP-It! Language-Guided Video SummarizationCode0
CSTA: CNN-based Spatiotemporal Attention for Video SummarizationCode0
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness RewardCode0
Discriminative Feature Learning for Unsupervised Video SummarizationCode0
Test-Time Training with Self-Supervision for Generalization under Distribution ShiftsCode0
Improving Sequential Determinantal Point Processes for Supervised Video Summarization—0
Joint Video Summarization and Moment Localization by Cross-Task Sample Transfer—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
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization—0
Hierarchical Multimodal Transformer to Summarize Videos—0
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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