用AI预标注提升视频标注效率，平均省时35%。
An Evaluation of Hybrid Annotation Workflows on High-Ambiguity Spatiotemporal Video Footage
- 用调优的编码器生成自动预标注，辅助人工标注流程。
- 18人单轮测试中，72%参与者标注时间减少35%。
- 提供可量化评估框架，平衡速度与标注准确率。
手动标注仍是高质量密集时间视频数据集的金标准，但耗时严重。视觉语言模型可辅助人工标注，加速该过程。本文报告了经过调优的编码器生成的自动预标注对‘人在回路’标注工作流的影响。一项包含18名志愿者的单轮测试显示，该工作流使多数（72%）参与者标注时间减少35%。除效率提升外，我们还提出一个严谨的基准评估框架，用于量化算法速度与人工验证完整性之间的权衡。
原文摘要 · Abstract (English)
Manual annotation remains the gold standard for high-quality, dense temporal video datasets, yet it is inherently time-consuming. Vision-language models can aid human annotators and expedite this process. We report on the impact of automatic Pre-Annotations from a tuned encoder on a Human-in-the-Loop labeling workflow for video footage. Quantitative analysis in a study of a single-iteration test involving 18 volunteers demonstrates that our workflow reduced annotation time by 35% for the majority (72%) of the participants. Beyond efficiency, we provide a rigorous framework for benchmarking AI-assisted workflows that quantifies trade-offs between algorithmic speed and the integrity of human verification.
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