Sifter：一种基于主题的视频大规模处理的混合工作流
Sifter: A Hybrid Workflow for Theme-based Video Curation at Scale
摘要：
用户生成的内容平台将其庞大的知识库整理成专题汇编，便于发现高质量的材料。寻求严格编辑控制的平台会雇佣人员进行这种管理，但这个过程涉及耗时的日常任务，例如筛选数千个视频。我们介绍Sifter，这是一个系统，它通过将自动化技术与人工管道相结合来改进策展过程，该管道可以浏览、选择并就汇编中包含哪些视频达成协议。我们通过从超过34，000个用户生成的视频中创建12个汇编来评估Sifter。Sifter的速度比专门的策展人快三倍多，其输出的质量也相当。我们反思了Sifter引入的挑战和机遇，以告知需要对视频进行大规模主观判断的内容策展系统的设计。
英文摘要：
User-generated content platforms curate their vast repositories into thematic compilations that facilitate the discovery of high-quality material. Platforms that seek tight editorial control employ people to do this curation, but this process involves time-consuming routine tasks, such as sifting through thousands of videos. We introduce Sifter, a system that improves the curation process by combining automated techniques with a human-powered pipeline that browses, selects, and reaches an agreement on what videos to include in a compilation. We evaluated Sifter by creating 12 compilations from over 34,000 user-generated videos. Sifter was more than three times faster than dedicated curators, and its output was of comparable quality. We reflect on the challenges and opportunities introduced by Sifter to inform the design of content curation systems that need subjective human judgments of videos at scale.
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。
