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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 280 papers

TitleStatusHype
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
Human Pose Estimation using Motion Priors and Ensemble Models—0
Real-time Video Summarization on Commodity Hardware—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
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