SOTAVerified

Video Summarization

Video Summarization aims to generate a short synopsis that summarizes the video content by selecting its most informative and important parts. The produced summary is usually composed of a set of representative video frames (a.k.a. video key-frames), or video fragments (a.k.a. video key-fragments) that have been stitched in chronological order to form a shorter video. The former type of a video summary is known as video storyboard, and the latter type is known as video skim.

Source: Video Summarization Using Deep Neural Networks: A Survey Image credit: iJRASET

Papers

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Showing 151–200 of 280 papers

TitleStatusHype
Siamese Tracking with Lingual Object ConstraintsCode0
SalSum: Saliency-based Video Summarization using Generative Adversarial Networks—0
Spatio-Temporal Stability Analysis in Satellite Image Times SeriesCode0
Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance—0
Online Learnable Keyframe Extraction in Videos and its Application with Semantic Word Vector in Action Recognition—0
Exploring global diverse attention via pairwise temporal relation for video summarization—0
Image Conditioned Keyframe-Based Video Summarization Using Object Detection—0
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization—0
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
SumGraph: Video Summarization via Recursive Graph Modeling—0
Submodular Maximization in Clean Linear Time—0
Transforming Multi-Concept Attention into Video Summarization—0
A Survey on Patch-based Synthesis: GPU Implementation and Optimization—0
Text Synopsis Generation for Egocentric Videos—0
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention—0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
Unsupervised Video Summarization via Attention-Driven Adversarial LearningCode0
ILS-SUMM: Iterated Local Search for Unsupervised Video SummarizationCode0
An Attention-Based Speaker Naming Method for Online Adaptation in Non-Fixed Scenarios—0
A Graph-based Ranking Approach to Extract Key-frames for Static Video Summarization—0
Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction—0
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video SummarizationCode0
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation—0
Multi-modal Deep Analysis for Multimedia—0
Unsupervised video summarization framework using keyframe extraction and video skimmingCode0
Video Skimming: Taxonomy and Comprehensive Survey—0
Meta Learning for Task-Driven Video Summarization—0
A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks—0
Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers—0
Hierarchical Recurrent Neural Network for Video Summarization—0
A General Framework for Edited Video and Raw Video Summarization—0
NLP Driven Ensemble Based Automatic Subtitle Generation and Semantic Video Summarization Technique—0
Video Object Segmentation and Tracking: A Survey—0
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization—0
FrameRank: A Text Processing Approach to Video Summarization—0
Rethinking the Evaluation of Video SummariesCode0
Video Summarization via Actionness Ranking—0
A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities—0
Real-time Video Summarization on Commodity Hardware—0
Human Pose Estimation using Motion Priors and Ensemble Models—0
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance—0
Summarizing Videos with AttentionCode0
SUSiNet: See, Understand and Summarize it—0
Sequence-to-Segment Networks for Segment Detection—0
Multi-Stream Dynamic Video SummarizationCode0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
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All datasetsSumMeTvSumShot2Story20KQuery-Focused Video Summarization DatasetMr. HiSumVideoXum

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PGL-SUMF1-score (Canonical)55.6—Unverified
2RR-STGF1-score (Canonical)54.5—Unverified
3DSNetF1-score (Canonical)53—Unverified
4VASNetF1-score (Canonical)49.71—Unverified
5M-AVSF1-score (Canonical)44.4—Unverified
6CSTAKendall's Tau0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1RR-STGF1-score (Canonical)63—Unverified
2DSNetF1-score (Canonical)62.1—Unverified
3VASNetF1-score (Canonical)61.42—Unverified
4PGL-SUMF1-score (Canonical)61—Unverified
5M-AVSF1-score (Canonical)61—Unverified
6CSTAKendall's Tau0.19—Unverified
#ModelMetricClaimedVerifiedStatus
1Shotluck-Holmes (3.1B)CIDEr152.3—Unverified
2Shotluck-Holmes (3.1B)CIDEr63.2—Unverified
3SUM-shotCIDEr8.6—Unverified
#ModelMetricClaimedVerifiedStatus
1EgoVLPv2F1 (avg)52.08—Unverified
2EgoVLPF1 (avg)49.72—Unverified
#ModelMetricClaimedVerifiedStatus
1PGL-SUMMAP (50%)61.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VTSUM-BLIP1 shot Micro-F123.5—Unverified