Papers with Code 아카이브
Unsupervised Video Summarization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 18
Unsupervised video summarization approaches overcome the need for ground-truth data (whose production requires time-demanding and laborious manual annotation procedures), based on learning mechanisms that require only an adequately large collection of original videos for their training. Specifically, the training is based on heuristic rules, like the sparsity, the representativeness, and the diversity of the utilized input features/characteristics.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Video Summarization using Denoising Diffusion Probabilistic Model
Personalized Video Summarization by Multimodal Video Understanding
Unsupervised Video Summarization via Reinforcement Learning and a Trained Evaluator
Cluster-based Video Summarization with Temporal Context Awareness
Enhancing Video Summarization with Context Awareness
Unsupervised Video Summarization via Iterative Training and Simplified GAN
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score
Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization
Masked Autoencoder for Unsupervised Video Summarization
Learning to Summarize Videos by Contrasting Clips
Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames
ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN
Self-Attention Recurrent Summarization Network with Reinforcement Learning for Video Summarization Task
Unsupervised Video Summarization via Multi-source Features
Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network
AC-SUM-GAN: Connecting Actor-Critic and Generative Adversarial Networks for Unsupervised Video Summarization
ILS-SUMM: Iterated Local Search for Unsupervised Video Summarization
A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization
Unsupervised video summarization framework using keyframe extraction and video skimming
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
FrameRank: A Text Processing Approach to Video Summarization
Discriminative Feature Learning for Unsupervised Video Summarization
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.