  The video action segmentation task is regularly explored under weaker forms
of supervision, such as transcript supervision, where a list of actions is
easier to obtain than dense frame-wise labels. In this formulation, the task
presents various challenges for sequence modeling approaches due to the
emphasis on action transition points, long sequence lengths, and frame
contextualization, making the task well-posed for transformers. Given
developments enabling transformers to scale linearly, we demonstrate through
our architecture how they can be applied to improve action alignment accuracy
over the equivalent RNN-based models with the attention mechanism focusing
around salient action transition regions. Additionally, given the recent focus
on inference-time transcript selection, we propose a supplemental transcript
embedding approach to select transcripts more quickly at inference-time.
Furthermore, we subsequently demonstrate how this approach can also improve the
overall segmentation performance. Finally, we evaluate our proposed methods
across the benchmark datasets to better understand the applicability of
transformers and the importance of transcript selection on this video-driven
weakly-supervised task.
