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.

通过引入贝叶斯 - VSLNet，本技术报告解决了在大段未修剪的自我中心视频中根据自然语言描述定位活动的时间边界的挑战。我们的模型通过在推理过程中引入新颖的贝叶斯时间顺序先验，显著改进了传统模型，提高了时刻预测的准确性。我们的评估表明，在 Ego4D Goal-Step 数据集上具有卓越的性能，测试集中 Recall Top-1 为 35.18（在 0.3 的 IoU 下）和 20.48（在 0.5 的 IoU 下），达到了最先进的结果。