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 51–75 of 280 papers

TitleStatusHype
SD-VSum: A Method and Dataset for Script-Driven Video SummarizationCode0
Video Summarization with Large Language Models—0
Automatic Detection of Intro and Credits in Video using CLIP and Multihead Attention—0
FaVChat: Unlocking Fine-Grained Facail Video Understanding with Multimodal Large Language Models—0
A Novel Trustworthy Video Summarization Algorithm Through a Mixture of LoRA Experts—0
Parameter-free Video Segmentation for Vision and Language Understanding—0
CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion—0
Integrate the temporal scheme for unsupervised video summarization via attention mechanismCode0
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications—0
What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific PresentationsCode0
FullTransNet: Full Transformer with Local-Global Attention for Video Summarization—0
Query-centric Audio-Visual Cognition Network for Moment Retrieval, Segmentation and Step-Captioning—0
Agent-based Video Trimming—0
Video Summarization using Denoising Diffusion Probabilistic Model—0
Personalized Video Summarization by Multimodal Video Understanding—0
Your Interest, Your Summaries: Query-Focused Long Video SummarizationCode0
Exploring Efficient Foundational Multi-modal Models for Video Summarization—0
Video Summarization Techniques: A Comprehensive Review—0
Realizing Video Summarization from the Path of Language-based Semantic Understanding—0
Does SpatioTemporal information benefit Two video summarization benchmarks?Code0
EDSNet: Efficient-DSNet for Video Summarization—0
Personalized Video Summarization using Text-Based Queries and Conditional Modeling—0
EgoSonics: Generating Synchronized Audio for Silent Egocentric Videos—0
Multimodal Language Models for Domain-Specific Procedural Video Summarization—0
Unsupervised Video Summarization via Reinforcement Learning and a Trained Evaluator—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