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 101–150 of 280 papers

TitleStatusHype
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
"Previously on ..." From Recaps to Story Summarization—0
Previously on ... From Recaps to Story Summarization—0
Prompts to Summaries: Zero-Shot Language-Guided Video Summarization—0
Query-Aware Sparse Coding for Multi-Video Summarization—0
Query-based Video Summarization with Pseudo Label Supervision—0
Query-centric Audio-Visual Cognition Network for Moment Retrieval, Segmentation and Step-Captioning—0
Query-Conditioned Three-Player Adversarial Network for Video Summarization—0
Query-Focused Extractive Video Summarization—0
Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach—0
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization—0
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework—0
Realizing Video Summarization from the Path of Language-based Semantic Understanding—0
Real-time Video Summarization on Commodity Hardware—0
Reconstructive Sequence-Graph Network for Video Summarization—0
REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing—0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization—0
Representative Selection for Big Data via Sparse Graph and Geodesic Grassmann Manifold Distance—0
Retrospective Encoders for Video Summarization—0
Role of Audio in Audio-Visual Video Summarization—0
Saliency-based Video Summarization for Face Anti-spoofing—0
SalSum: Saliency-based Video Summarization using Generative Adversarial Networks—0
Scaling Submodular Maximization via Pruned Submodularity Graphs—0
Scaling Up Video Summarization Pretraining with Large Language Models—0
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
Segmentation of Bleeding Regions in Wireless Capsule Endoscopy Images an Approach for inside Capsule Video Summarization—0
Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Semantic Video Trailers—0
Sequence-to-Segment Networks for Segment Detection—0
Show and Recall: Learning What Makes Videos Memorable—0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video—0
Story-Driven Summarization for Egocentric Video—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
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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