What do I Annotate Next? An Empirical Study of Active Learning for Action Localization

What do I Annotate Next? An Empirical Study of Active Learning for Action Localization

​Fabian Caba Heilbron, Joon-Young Lee, Hailin Jin, Bernard Ghanem,​ 
"What do I Annotate Next? An Empirical Study of Active Learning for Action Localization​" 
European Conference on Computer Vision (ECCV 2018)​​​
Fabian Caba Heilbron, Joon-Young Lee, Hailin Jin, Bernard Ghanem
video understanding, temporal action localization, active learning, video annotation
2018
​Despite tremendous progress achieved in temporal action localization, state-of-the-art methods still struggle to train accurate models when annotated data is scarce. In this paper, we introduce a novel active learning framework for temporal localization that aims to mitigate this data dependency issue. We equip our framework with active selection functions that can reuse knowledge from previously annotated datasets. We study the performance of two state-of-the-art active selection functions as well as two widely used active learning baselines. To validate the effectiveness of each one of these selection functions, we conduct simulated experiments on ActivityNet. We find that using previously acquired knowledge as a bootstrapping source is crucial for active learners aiming to localize actions. When equipped with the right selection function, our proposed framework exhibits significantly better performance than standard active learning strategies, such as uncertainty sampling. Finally, we employ our framework to augment the newly compiled Kinetics action dataset with ground-truth temporal annotations. As a result, we collect Kinetics-Localization, a novel large-scale dataset for temporal action localization, which contains more than 15K YouTube videos.