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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 280 papers

TitleStatusHype
AudioVisual Video Summarization—0
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization—0
Reconstructive Sequence-Graph Network for Video Summarization—0
TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domainsCode1
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video SummarizationCode0
Supervised Video Summarization via Multiple Feature Sets with Parallel AttentionCode1
The Power of Subsampling in Submodular Maximization—0
A Comprehensive Review of the Video-to-Text ProblemCode1
Learning Discriminative Prototypes with Dynamic Time WarpingCode1
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Visual Question Answering: which investigated applications?Code0
Audiovisual Highlight Detection in Videos—0
Efficient Video Summarization Framework using EEG and Eye-tracking Signals—0
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization—0
Video Summarization: Study of various techniques—0
Video Summarization Using Deep Neural Networks: A Survey—0
Multiple Pairwise Ranking Networks for Personalized Video Summarization—0
Movie Summarization via Sparse Graph ConstructionCode1
DSNet: A Flexible Detect-to-Summarize Network for Video SummarizationCode1
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events—0
Siamese Tracking with Lingual Object ConstraintsCode0
SalSum: Saliency-based Video Summarization using Generative Adversarial Networks—0
AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video SummarizationCode1
Spatio-Temporal Stability Analysis in Satellite Image Times SeriesCode0
Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance—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
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