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 126–150 of 280 papers

TitleStatusHype
Beyond the Frame: Single and mutilple video summarization method with user-defined length—0
Causalainer: Causal Explainer for Automatic Video Summarization—0
Causal Video Summarizer for Video Exploration—0
CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion—0
CNN-Based Prediction of Frame-Level Shot Importance for Video Summarization—0
Common Action Discovery and Localization in Unconstrained Videos—0
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
Conditional Modeling Based Automatic Video Summarization—0
Co-Regularized Deep Representations for Video Summarization—0
Creating Summaries from User Videos—0
CSTA: CNN-based Spatiotemporal Attention for Video Summarization—0
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization—0
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization—0
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance—0
Detecting Engagement in Egocentric Video—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
Diverse and Coherent Paragraph Generation from Images—0
Stream Clipper: Scalable Submodular Maximization on Stream—0
Subset Selection and Summarization in Sequential Data—0
SumGraph: Video Summarization via Recursive Graph Modeling—0
Summarization of User-Generated Sports Video by Using Deep Action Recognition Features—0
Summary Transfer: Exemplar-based Subset Selection for Video Summarization—0
SUSiNet: See, Understand and Summarize it—0
Temporally Coherent Bayesian Models for Entity Discovery in Videos by Tracklet Clustering—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