# t-EVA: Time-Efficient t-SNE Video Annotation

arXiv: https://arxiv.org/abs/2011.13202 · HTML: https://mlsanity.com/paper/2011.13202/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [A Tour of Visualization Techniques for Computer Vision Datasets](https://mlsanity.com/paper/2204.08601/) (2022)
- [HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions](https://mlsanity.com/paper/2409.10641/) (2024)

## Abstract (arXiv)

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.