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Efficient Two-Step Networks for Temporal Action Segmentation
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DOI：10.1016/j.neucom.2021.04.121.png)
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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. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Temporal action segmentation
Temporal series pyramid networks
Local Burr suppression
Two-step method
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2.5W
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