  The goal of the Step Grounding task is to locate temporal boundaries of
activities based on natural language descriptions. This technical report
introduces a Bayesian-VSLNet to address the challenge of identifying such
temporal segments in lengthy, untrimmed egocentric videos. Our model
significantly improves upon traditional models by incorporating a novel
Bayesian temporal-order prior during inference, enhancing the accuracy of
moment predictions. This prior adjusts for cyclic and repetitive actions within
videos. Our evaluations demonstrate superior performance over existing methods,
achieving state-of-the-art results on the Ego4D Goal-Step dataset with a 35.18
Recall Top-1 at 0.3 IoU and 20.48 Recall Top-1 at 0.5 IoU on the test set.
