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
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
Text Synopsis Generation for Egocentric Videos—0
The Power of Subsampling in Submodular Maximization—0
TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency—0
Transforming Multi-Concept Attention into Video Summarization—0
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness—0
TriPSS: A Tri-Modal Keyframe Extraction Framework Using Perceptual, Structural, and Semantic Representations—0
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation—0
TVSum: Summarizing Web Videos Using Titles—0
Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance—0
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder—0
Unsupervised Transcript-assisted Video Summarization and Highlight Detection—0
Unsupervised Video Summarization via Reinforcement Learning and a Trained Evaluator—0
Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network—0
Use of Affective Visual Information for Summarization of Human-Centric Videos—0
V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning—0
Video Co-Summarization: Video Summarization by Visual Co-Occurrence—0
Video Object Segmentation and Tracking: A Survey—0
Video Skimming: Taxonomy and Comprehensive Survey—0
Video Summarization by Learning Submodular Mixtures of Objectives—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