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 151–175 of 280 papers

TitleStatusHype
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
Multi-modal Summarization for Video-containing DocumentsCode1
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
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework—0
SumGraph: Video Summarization via Recursive Graph Modeling—0
Submodular Maximization in Clean Linear Time—0
Transforming Multi-Concept Attention into Video Summarization—0
Ultrasound Video Summarization using Deep Reinforcement LearningCode1
A Survey on Patch-based Synthesis: GPU Implementation and Optimization—0
Text Synopsis Generation for Egocentric Videos—0
Query-controllable Video SummarizationCode1
Group Activity Recognition by Using Effective Multiple Modality Relation Representation With Temporal-Spatial Attention—0
Convolutional Hierarchical Attention Network for Query-Focused Video SummarizationCode1
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
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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
4M-AVSF1-score (Canonical)61—Unverified
5PGL-SUMF1-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