期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 22, 期 2, 页码 554-565出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2019.2930041
关键词
Visualization; Task analysis; Semantics; Streaming media; Recurrent neural networks; Measurement; Natural languages; Video storytelling; video captioning; sentence retrieval; multimodal embedding learning
资金
- National Research Foundation, Prime Minister's Office, Singapore under its Strategic Capability Research Centres Funding Initiative
Bridging vision and natural language is a longstanding goal in computer vision and multimedia research. While earlier works focus on generating a single-sentence description for visual content, recent works have studied paragraph generation. In this paper, we introduce the problem of video storytelling, which aims at generating coherent and succinct stories for long videos. Video storytelling introduces new challenges, mainly due to the diversity of the story and the length and complexity of the video. We propose novel methods to address the challenges. First, we propose a context-aware framework for multimodal embedding learning, where we design a residual bidirectional recurrent neural network to leverage contextual information from past and future. The multimodal embedding is then used to retrieve sentences for video clips. Second, we propose a Narrator model to select clips that are representative of the underlying storyline. The Narrator is formulated as a reinforcement learning agent, which is trained by directly optimizing the textual metric of the generated story. We evaluate our method on the video story dataset, a new dataset that we have collected to enable the study. We compare our method with multiple state-of-the-art baselines and show that our method achieves better performance, in terms of quantitative measures and user study.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据