PHD-GIF：用于自动创建GIF的个性化高光检测
PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation
摘要：
高光检测模型通常被训练以识别使视觉内容吸引公众或使公众感兴趣的线索，目的是将视频减少到这样的时刻。然而，视频片段或图像的“兴趣度”是主观的。因此，这样的突出部分模型为个体用户提供有限相关性的结果。另一方面，为每个用户训练一个模型是低效的，并且需要大量通常不可用的个人信息。为了克服这些局限性，我们提出了一个全球排名模型的条件下，每个特定用户的利益。我们的模型不是为每个用户训练一个模型，而是通过其输入进行个性化，这使得它能够有效地调整其预测，只给出几个特定于用户的示例。为了训练这个模型，我们创建了一个大规模的用户数据集和他们创建的GIF，为我们提供了他们兴趣的准确指示。我们的实验表明，使用用户历史大大提高了预测精度。在我们的850个视频的测试集上，我们的模型相对于通用高光检测器将召回率提高了8%。此外，即使只有一个特定于个人的例子，我们的方法也比用户不可知的基线更精确。
英文摘要：
Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video to such moments. However, the "interestingness" of a video segment or image is subjective. Thus, such highlight models provide results of limited relevance for the individual user. On the other hand, training one model per user is inefficient and requires large amounts of personal information which is typically not available. To overcome these limitations, we present a global ranking model which conditions on each particular user's interests. Rather than training one model per user, our model is personalized via its inputs, which allows it to effectively adapt its predictions, given only a few user-specific examples. To train this model, we create a large-scale dataset of users and the GIFs they created, giving us an accurate indication of their interests. Our experiments show that using the user history substantially improves the prediction accuracy. On our test set of 850 videos, our model improves the recall by 8% with respect to generic highlight detectors. Furthermore, our method proves more precise than the user-agnostic baselines even with just one person-specific example.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
