论文标题
T-eva：时间效率的T-SNE视频注释
t-EVA: Time-Efficient t-SNE Video Annotation
论文作者
论文摘要
由于几个大型视频数据集的可用性，在过去几年中，视频理解已受到更多关注。但是，注释大规模的视频数据集是成本密集的。在这项工作中，我们建议使用时空特征相似性和T-SNE维度降低的时间效率的视频注释方法，以大大加快注释过程。基于特征相似性，在二维空间中彼此接近不同视频的相同动作有助于注释器对组标签视频剪辑。我们在两个活动网（v1.3）和Sports-1M数据集的两个子集上评估我们的方法。我们表明，T-EVA可以在保持视频分类的测试准确性的同时胜过其他视频注释工具。
Video understanding has received more attention in the past few years due to the availability of several large-scale video datasets. However, annotating large-scale video datasets are cost-intensive. In this work, we propose a time-efficient video annotation method using spatio-temporal feature similarity and t-SNE dimensionality reduction to speed up the annotation process massively. Placing the same actions from different videos near each other in the two-dimensional space based on feature similarity helps the annotator to group-label video clips. We evaluate our method on two subsets of the ActivityNet (v1.3) and a subset of the Sports-1M dataset. We show that t-EVA can outperform other video annotation tools while maintaining test accuracy on video classification.
