Due to boundary ambiguity and over-segmentation issues, identifying all the frames in long untrimmed videos is still challenging. To address these problems, we present the Efficient Two-Step Network (ETSN) with two components. The first step of ETSN is Efficient Temporal Series Pyramid Networks (ETSPNet) that capture both local and global frame-level features and provide accurate predictions of segmentation boundaries. The second step is a novel unsupervised approach called Local Burr Suppression (LBS), which significantly reduces the over-segmentation errors. Our empirical evaluations on the benchmarks including 50Salads, GTEA and Breakfast dataset demonstrate that ETSN outperforms the current state-of-the-art methods by a large margin.

由于边界模棱两可和过度分割问题，在未修剪的长视频中识别所有帧仍然具有挑战性。为了解决这些问题，我们提出了由两部分组成的高效两步式网络（ETSN）。ETSN的第一步是高效的时间序列金字塔网络（ETSPNet），该网络可以捕获局部和全局帧级特征，并提供对分段边界的准确预测。第二步是一种新颖的无监督方法，称为局部毛刺抑制（LBS），可显着减少过度分割错误。我们对包括50Salads，GTEA和Breakfast数据集在内的基准进行的经验评估表明，ETSN在很大程度上优于当前的最新方法。