Supervised Video Summarization
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Benchmarks
Most implemented
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
Align and Attend: Multimodal Summarization with Dual Contrastive Losses
Progressive Video Summarization via Multimodal Self-supervised Learning
Video Joint Modelling Based on Hierarchical Transformer for Co-summarization
Papers
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
The increasing ubiquity of video content and the corresponding demand for efficient access to meaningful information have elevated video summarization and video highlights as a vital research area. However, many state-of…
Self-Supervised LearningSupervised Video SummarizationVideo SummarizationFullTransNet: Full Transformer with Local-Global Attention for Video Summarization
Video summarization mainly aims to produce a compact, short, informative, and representative synopsis of raw videos, which is of great importance for browsing, analyzing, and understanding video content. Dominant video s…
DecoderSupervised Video SummarizationVideo SummarizationCSTA: CNN-based Spatiotemporal Attention for Video Summarization
Video summarization aims to generate a concise representation of a video, capturing its essential content and key moments while reducing its overall length. Although several methods employ attention mechanisms to handle …
Supervised Video SummarizationVideo SummarizationLanguage-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video
Current video summarization methods rely heavily on supervised computer vision techniques, which demands time-consuming and subjective manual annotations. To overcome these limitations, we investigated self-supervised vi…
DiversitySupervised Video SummarizationVideo SummarizationAlign and Attend: Multimodal Summarization with Dual Contrastive Losses
The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Unlike the unimodal summarization, the multimodal summarization task explicitly levera…
Extractive Text SummarizationSupervised Video SummarizationVideo SummarizationRelational Reasoning Over Spatial-Temporal Graphs for Video Summarization
In this paper, we propose a dynamic graph modeling approach to learn spatial-temporal representations for video summarization. Most existing video summarization methods extract image-level features with ImageNet pre-trai…
Graph ClassificationRelationRelational ReasoningSupervised Video Summarization+1