找到正确的时刻:通过任务组合的人类辅助拖车创建.
IEEE transactions on pattern analysis and machine intelligence
|October 17, 2023
概括
研究人员开发了一种新的方法,通过将电影模拟成图形来识别电影预告片的时刻. 这种结合叙事结构和情绪分析的方法,优于现有的自动和手动技术,可以有效地创建拖车.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 电影预告片对于电影推广至关重要,引入叙事,情绪和风格以吸引观众.
- 拖车的创建是复杂的,因为需要平衡多个沟通功能.
- 确定最佳的"拖车时刻" (拖车拍摄) 是自动化或半自动化拖车生成的一个关键挑战.
研究的目的:
- 开发和评估一种用于识别电影中最佳预告片时刻的新方法.
- 通过自动化和人-in-the-loop方法来提高拖车制造的效率和质量.
主要方法:
- 电影以图形的形式呈现,镜头作为节点,语义关系作为边缘,通过联合对比训练来学习电影剧本数据.
- 该方法将拖车时刻选择分解为叙事结构识别和情绪预测.
- 一个无监督的图形穿越算法根据学习的语义关系选择拖车时刻.
主要成果:
- 人类法官更喜欢不受监督的算法所选择的预告片段,而不是受监督的方法.
- 一个交互式工具,利用可解释的标准,使用户能够在不到30分钟的时间内选择优秀的拖车镜头.
- 该工具的输出可与专家手工选择相美,优于全自动方法.
结论:
- 拟议的基于图形的方法通过整合叙事和情感分析,有效地识别引人注目的预告片时刻.
- 这种可解释,人为循环的工具提高了拖车创建效率和质量,使专家级别的选择民主化.
- 这项研究在自动视频总结和媒体营销内容选择方面取得了重大进展.
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