Papers with Code 아카이브
Video Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 6
- 보존된 평가 기록
- 18
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 <br> <span style="color:grey; opacity: 0.6">Image credit: iJRASET</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods
Shotluck Holmes: A Family of Efficient Small-Scale Large Language Vision Models For Video Captioning and Summarization
VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
CSTA: CNN-based Spatiotemporal Attention for Video Summarization
"Previously on ..." From Recaps to Story Summarization
An Integrated Framework for Multi-Granular Explanation of Video Summarization
Language-Guided Self-Supervised Video Summarization Using Text Semantic Matching Considering the Diversity of the Video
Pegasus-v1 Technical Report
V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning
VideoSAGE: Video Summarization with Graph Representation Learning
Cluster-based Video Summarization with Temporal Context Awareness
Enhancing Video Summarization with Context Awareness
Scaling Up Video Summarization Pretraining with Large Language Models
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding
FastPerson: Enhancing Video Learning through Effective Video Summarization that Preserves Linguistic and Visual Contexts
Large Model based Sequential Keyframe Extraction for Video Summarization
ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video
Beyond the Frame: Single and mutilple video summarization method with user-defined length
Shot2Story20K: A New Benchmark for Comprehensive Understanding of Multi-shot Videos
An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos
Facilitating the Production of Well-tailored Video Summaries for Sharing on Social Media
A Challenging Multimodal Video Summary: Simultaneously Extracting and Generating Keyframe-Caption Pairs from Video
Video Summarization: Towards Entity-Aware Captions
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames
Conditional Modeling Based Automatic Video Summarization
Dynamic Non-monotone Submodular Maximization
Unsupervised Video Summarization via Iterative Training and Simplified GAN
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
DeVAn: Dense Video Annotation for Video-Language Models
Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score
Saliency-based Video Summarization for Face Anti-spoofing
UniVTG: Towards Unified Video-Language Temporal Grounding
Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization
EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.