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

Showing 2650 of 280 papers

TitleStatusHype
VideoSum: A Python Library for Surgical Video SummarizationCode1
VideoXum: Cross-modal Visual and Textural Summarization of VideosCode1
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video SummarizationCode1
DSNet: A Flexible Detect-to-Summarize Network for Video SummarizationCode1
Discriminative Latent Semantic Graph for Video CaptioningCode1
AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video SummarizationCode1
Do Language Models Understand Time?Code1
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the BackboneCode1
Movie Summarization via Sparse Graph ConstructionCode1
Hierarchical Video-Moment Retrieval and Step-CaptioningCode1
IntentVizor: Towards Generic Query Guided Interactive Video SummarizationCode1
Learning Discriminative Prototypes with Dynamic Time WarpingCode1
A Comprehensive Review of the Video-to-Text ProblemCode1
Ultrasound Video Summarization using Deep Reinforcement LearningCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative ScoreCode1
Multimodal Summarization of User-Generated VideosCode1
Convolutional Hierarchical Attention Network for Query-Focused Video SummarizationCode1
Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers0
A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video0
A Multi-stage deep architecture for summary generation of soccer videos0
A Survey on Patch-based Synthesis: GPU Implementation and Optimization0
A Memory Network Approach for Story-Based Temporal Summarization of 360° Videos0
A Framework towards Domain Specific Video Summarization0
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PGL-SUMF1-score (Canonical)55.6Unverified
2RR-STGF1-score (Canonical)54.5Unverified
3DSNetF1-score (Canonical)53Unverified
4VASNetF1-score (Canonical)49.71Unverified
5M-AVSF1-score (Canonical)44.4Unverified
6CSTAKendall's Tau0.25Unverified
#ModelMetricClaimedVerifiedStatus
1RR-STGF1-score (Canonical)63Unverified
2DSNetF1-score (Canonical)62.1Unverified
3VASNetF1-score (Canonical)61.42Unverified
4PGL-SUMF1-score (Canonical)61Unverified
5M-AVSF1-score (Canonical)61Unverified
6CSTAKendall's Tau0.19Unverified
#ModelMetricClaimedVerifiedStatus
1Shotluck-Holmes (3.1B)CIDEr152.3Unverified
2Shotluck-Holmes (3.1B)CIDEr63.2Unverified
3SUM-shotCIDEr8.6Unverified
#ModelMetricClaimedVerifiedStatus
1EgoVLPv2F1 (avg)52.08Unverified
2EgoVLPF1 (avg)49.72Unverified
#ModelMetricClaimedVerifiedStatus
1PGL-SUMMAP (50%)61.6Unverified
#ModelMetricClaimedVerifiedStatus
1VTSUM-BLIP1 shot Micro-F123.5Unverified