机器学习正在改变视频编辑行业。计算机视觉的最新进展已升级视频编辑任务，例如智能重新构图，旋转镜，颜色分级或应用数字化妆。但是，大多数解决方案都集中在视频操作和VFX上。这项工作介绍了视频编辑，数据集和基准测试的解剖结构，以促进AI辅助视频编辑研究。我们的基准套件专注于视频编辑任务，除了视觉效果之外，例如自动录像组织和辅助视频组装。为了对这些方面进行研究，我们注释了超过150万的标签，并从196176年从电影场景中取样了摄影作品。我们为每个任务建立竞争性基线方法和详细分析。我们希望我们的作品能够对AI辅助视频编辑的未经展开的领域进行创新的研究。

Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often, multiple clinical outcomes are measured during an RCT, each having a potentially heterogeneous effect. Recently there has been high interest in identifying subgroups from HTEs, however, there has been less focus on developing tools in settings where there are multiple outcomes. In this work, we propose a framework for partitioning the covariate space to identify subgroups across multiple outcomes based on the joint CIs. We test our algorithm on synthetic and semi-synthetic data where there are two outcomes, and demonstrate that our algorithm is able to capture the HTE in both outcomes simultaneously.