Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
文献类型：会议论文
| 作者 | Kaiyang Zhou; Yu Qiao |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 美国 |
| 英文摘要 | Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos. In this paper, we formulate video summarization as a sequential decisionmaking process and develop a deep summarization network (DSN) to summarize videos. DSN predicts for each video frame a probability, which indicates how likely a frame is selected, and then takes actions based on the probability distributions to select frames, forming video summaries. To train our DSN, we propose an end-to-end, reinforcement learningbased framework, where we design a novel reward function that jointly accounts for diversity and representativeness of generated summaries and does not rely on labels or user interactions at all. During training, the reward function judges how diverse and representative the generated summaries are, while DSN strives for earning higher rewards by learning to produce more diverse and more representative summaries. Since labels are not required, our method can be fully unsupervised. Extensive experiments on two benchmark datasets show that our unsupervised method not only outperforms other stateof-the-art unsupervised methods, but also is comparable to or even superior than most of published supervised approaches. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13683]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Kaiyang Zhou,Yu Qiao. Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward[C]. 见:. 美国. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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