Benchmarking Data Efficiency and Computational Efficiency of Temporal
  Action Localization Models
Jan Warchocki Teodor Oprescu Yunhan Wang Alexandru Damacus Paul Misterka Robert-Jan Bruintjes Attila Lengyel Ombretta Strafforello Jan van Gemert
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.