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.

本文研究了时间动作定位中当前深度学习模型在数据和计算能力受限制的情况下的性能表现，并发现 TemporalMaxer 模型在数据受限情况下表现最佳，推荐 TriDet 模型在训练时间受限时使用。另外，通过将不同长度的视频输入模型进行推理，发现 TemporalMaxer 模型需要较少的计算资源，可能是由于其简单的架构造成的。