Video annotation is a critical and time-consuming task in computer vision research and applications. This paper presents a novel annotation pipeline that uses pre-extracted features and dimensionality reduction to accelerate the temporal video annotation process. Our approach uses Hierarchical Stochastic Neighbor Embedding (HSNE) to create a multi-scale representation of video features, allowing annotators to efficiently explore and label large video datasets. We demonstrate significant improvements in annotation effort compared to traditional linear methods, achieving more than a 10x reduction in clicks required for annotating over 12 hours of video. Our experiments on multiple datasets show the effectiveness and robustness of our pipeline across various scenarios. Moreover, we investigate the optimal configuration of HSNE parameters for different datasets. Our work provides a promising direction for scaling up video annotation efforts in the era of video understanding.

本研究解决了计算机视觉领域中视频标注任务耗时且效率低下的问题。提出了一种新颖的注释管道，通过层次随机邻居嵌入（HSNE）创建视频特征的多尺度表示，从而显著提高标注效率，标注超过12小时视频所需的点击次数减少了10倍以上。该方法在多个数据集上的实验结果显示了其有效性和鲁棒性，为视频理解时代的大规模视频标注提供了有前景的方向。

HAVANA：用于加速视频标注的层次随机邻居嵌入