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Crowdsourced Time-sync Video Tagging using Temporal and Personalized Topic Modeling

机译：使用时间和个性化主题建模的众包时间同步视频标记

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Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting crowdsourced comments from video websites such as Nico Nico Douga, where videos are commented on by online crowd users in a time-sync manner. The challenge of the proposed application is that users with bias interact with one another frequently and bring noise into the data, while the comments are too sparse to compensate for the noise. Previous techniques are unable to handle this task well as they consider video semantics independently, which may overfit the sparse comments in each shot and thus fail to provide accurate modeling. To resolve these issues, we propose a novel temporal and personalized topic model that jointly considers temporal dependencies between video semantics, users' interaction in commenting, and users' preferences as prior knowledge. Our proposed model shares knowledge across video shots via users to enrich the short comments, and peels off user interaction and user bias to solve the noisy-comment problem. Log-likelihood analyses and user studies on large datasets show that the proposed model outperforms several state-of-the-art baselines in video tagging quality. Case studies also demonstrate our model's capability of extracting tags from the crowdsourced short and noisy comments.
机译：时间同步视频标记旨在自动为每个视频镜头生成标记。与标记整个视频片段的传统方案相比，它可以改善用户预览视频时间轴结构的体验。在本文中，我们提出了一个新的应用程序，该应用程序通过自动利用来自视频网站（如Nico Nico Douga）的众包评论来提取时间同步视频标签，在该网站上，在线人群用户以时间同步的方式对视频进行评论。所提出的应用程序的挑战在于，有偏见的用户经常彼此交互并将噪声带入数据中，而注释却过于稀疏而无法补偿噪声。先前的技术无法独立处理视频语义，因此无法很好地处理此任务，这可能会过度适合每个镜头中的稀疏注释，因此无法提供准确的建模。为了解决这些问题，我们提出了一种新颖的时间和个性化主题模型，该模型将视频语义，用户在评论中的交互以及用户的偏好之间的时间依赖性共同视为先验知识。我们提出的模型通过用户在视频快照之间共享知识，以丰富简短的评论，并消除用户交互和用户偏见，以解决嘈杂的评论问题。对数可能性分析和对大型数据集的用户研究表明，在视频标记质量方面，所提出的模型优于几种最先进的基准。案例研究还表明，我们的模型具有从众包简短且嘈杂的注释中提取标签的能力。

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