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 76–100 of 280 papers

TitleStatusHype
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization—0
CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion—0
A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features—0
Hierarchical Recurrent Neural Network for Video Summarization—0
Causal Video Summarizer for Video Exploration—0
Enhancing Video Summarization via Vision-Language Embedding—0
Causalainer: Causal Explainer for Automatic Video Summarization—0
Enhancing Video Memorability Prediction with Text-Motion Cross-modal Contrastive Loss and Its Application in Video Summarization—0
A Novel Technique for Evidence based Conditional Inference in Deep Neural Networks via Latent Feature Perturbation—0
Exploring Efficient Foundational Multi-modal Models for Video Summarization—0
Exploring global diverse attention via pairwise temporal relation for video summarization—0
Exploring Global Diversity and Local Context for Video Summarization—0
Facilitating the Production of Well-tailored Video Summaries for Sharing on Social Media—0
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts—0
ElasticPlay: Interactive Video Summarization with Dynamic Time Budgets—0
FaVChat: Unlocking Fine-Grained Facail Video Understanding with Multimodal Large Language Models—0
FrameRank: A Text Processing Approach to Video Summarization—0
From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection—0
Hierarchical Multimodal Transformer to Summarize Videos—0
HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization—0
Generating Natural Language Summaries for Multimedia—0
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization—0
Conditional Modeling Based Automatic Video Summarization—0
EgoSonics: Generating Synchronized Audio for Silent Egocentric Videos—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