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

Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models