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Efficient Two-Step Networks for Temporal Action Segmentation
Neurocomputing ( IF 5.5 ) Pub Date : 2021-05-04 , DOI: 10.1016/j.neucom.2021.04.121
Yunheng Li , Zhuben Dong , Kaiyuan Liu , Lin Feng , Lianyu Hu , Jie Zhu , Li Xu , Yuhan wang , Shenglan Liu

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.



中文翻译:

高效的两步网络,用于时间动作分段

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。

更新日期:2021-05-04
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