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Video summarisation by classification with deep reinforcement learning
Kaiyang Zhou
, Tao Xiang
, Andrea Cavallaro
Department of Computer Science
Research output
:
Contribution to conference
›
Conference paper
›
peer-review
18
Citations (Scopus)
Overview
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Dive into the research topics of 'Video summarisation by classification with deep reinforcement learning'. Together they form a unique fingerprint.
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Keyphrases
Deep Reinforcement Learning (deep RL)
100%
Video Summarization
100%
Reinforcement Learning
66%
Classification Network
66%
Recognizability
66%
Benchmark Dataset
33%
Summarization Technique
33%
Supervised Learning
33%
Unsupervised Learning
33%
Decision-making Process
33%
Ranking Method
33%
Learning-based
33%
Classification Results
33%
Long Sequence
33%
Reward-based
33%
Art Performance
33%
Category Labels
33%
Delayed Reward
33%
Weakly Supervised Method
33%
Summarization Network
33%
Sequential Decision Making
33%
Deep Q-learning
33%
Sparse Reward
33%
Computer Science
Deep Reinforcement Learning
100%
Video Summarization
100%
Reinforcement Learning
66%
Decision-Making
33%
Supervised Learning
33%
Unsupervised Learning
33%
classification result
33%
Art Performance
33%
Supervised Method
33%
Engineering
Video Summarization
100%
Deep Reinforcement Learning
100%
Reinforcement Learning
66%
Classification Network
66%
Summarization
33%
Q-Learning
33%
Chemical Engineering
Reinforcement Learning
100%
Deep Reinforcement Learning
100%
Unsupervised Learning
50%
Supervised Learning
50%