Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models

Conference paper (2023)

Authors

J. Warchocki Student
T. Oprescu Student
Y. Wang Student
A. Dămăcuș Student
P.M. Misterka Student
R. Bruintjes Pattern Recognition and Bioinformatics - EEMCS
A. Lengyel Pattern Recognition and Bioinformatics - EEMCS
O. Strafforello Pattern Recognition and Bioinformatics - EEMCS
J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2023 J. Warchocki, T. Oprescu, Y. Wang, A. Dămăcuș, P.M. Misterka, R. Bruintjes, A. Lengyel, O. Strafforello, J.C. van Gemert
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of- the-art deep learning models requires access to large amounts of data and computational power. However, gathering such data is challenging and computational resources might be limited. This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power. We measure data efficiency by training each model on a subset of the training set. We find that TemporalMaxer outperforms other models in data-limited settings. Furthermore, we recommend TriDet when training time is limited. To test the efficiency of the models during inference, we pass videos of different lengths through each model. We find that TemporalMaxer requires the least computational resources, likely due to its simple architecture.