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 151–200 of 280 papers

TitleStatusHype
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
Video Summarization in a Multi-View Camera Network—0
Video Summarization Overview—0
Video Summarization: Study of various techniques—0
Video Summarization Techniques: A Comprehensive Review—0
A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities—0
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net—0
Video Summarization Using Deep Neural Networks: A Survey—0
Video Summarization using Denoising Diffusion Probabilistic Model—0
Video Summarization Using Fully Convolutional Sequence Networks—0
Video Summarization via Actionness Ranking—0
Video Summarization with Attention-Based Encoder-Decoder Networks—0
Video Summarization with Large Language Models—0
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling—0
Viewpoint-aware Video Summarization—0
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel—0
Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction—0
VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering—0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
Multi-view Metric Learning for Multi-view Video Summarization—0
Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization—0
NEWSKVQA: Knowledge-Aware News Video Question Answering—0
NLP Driven Ensemble Based Automatic Subtitle Generation and Semantic Video Summarization Technique—0
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings—0
Online Learnable Keyframe Extraction in Videos and its Application with Semantic Word Vector in Action Recognition—0
Online Summarization via Submodular and Convex Optimization—0
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing—0
Parameter-free Video Segmentation for Vision and Language Understanding—0
Pegasus-v1 Technical Report—0
Personalized Video Summarization by Multimodal Video Understanding—0
Personalized Video Summarization using Text-Based Queries and Conditional Modeling—0
Predicting Important Objects for Egocentric Video Summarization—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