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Supervised Video Summarization
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
FullTransNet: Full Transformer with Local-Global Attention for Video Summarization
CSTA: CNN-based Spatiotemporal Attention for Video Summarization
Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization
Progressive Video Summarization via Multimodal Self-supervised Learning
Video Joint Modelling Based on Hierarchical Transformer for Co-summarization
A Stacking Ensemble Approach for Supervised Video Summarization
Hierarchical Multimodal Transformer to Summarize Videos
Use of Affective Visual Information for Summarization of Human-Centric Videos
CLIP-It! Language-Guided Video Summarization
Self-Attention Recurrent Summarization Network with Reinforcement Learning for Video Summarization Task
Supervised Video Summarization via Multiple Feature Sets with Parallel Attention
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
DSNet: A Flexible Detect-to-Summarize Network for Video Summarization
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Discriminative Feature Learning for Unsupervised Video Summarization
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
Video Summarization with Attention-Based Encoder-Decoder Networks
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