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 251–280 of 280 papers

TitleStatusHype
From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection—0
Scaling Submodular Maximization via Pruned Submodularity Graphs—0
Stream Clipper: Scalable Submodular Maximization on Stream—0
Video Summarization with Long Short-term MemoryCode0
Long-Term Identity-Aware Multi-Person Tracking for Surveillance Video Summarization—0
Detecting Engagement in Egocentric Video—0
Summary Transfer: Exemplar-based Subset Selection for Video Summarization—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
TVSum: Summarizing Web Videos Using Titles—0
Video Co-Summarization: Video Summarization by Visual Co-Occurrence—0
Video Summarization by Learning Submodular Mixtures of Objectives—0
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization—0
Predicting Important Objects for Egocentric Video Summarization—0
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel—0
Co-Regularized Deep Representations for Video Summarization—0
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video—0
Diverse Sequential Subset Selection for Supervised Video Summarization—0
Large-Margin Determinantal Point Processes—0
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy—0
Temporally Coherent Bayesian Models for Entity Discovery in Videos by Tracklet Clustering—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Joint Summarization of Large-scale Collections of Web Images and Videos for Storyline Reconstruction—0
Multi-view Metric Learning for Multi-view Video Summarization—0
Representative Selection for Big Data via Sparse Graph and Geodesic Grassmann Manifold Distance—0
VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering—0
An Enhanced Method For Evaluating Automatic Video Summaries—0
Creating Summaries from User Videos—0
Story-Driven Summarization for Egocentric Video—0
Large-Scale Video Summarization Using Web-Image Priors—0
Generating Natural Language Summaries for Multimedia—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