Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal
  Graph Convolutional Neural Networks   The ability to identify and temporally segment fine-grained actions in motion
capture sequences is crucial for applications in human movement analysis.
Motion capture is typically performed with optical or inertial measurement
systems, which encode human movement as a time series of human joint locations
and orientations or their higher-order representations. State-of-the-art action
segmentation approaches use multiple stages of temporal convolutions. The main
idea is to generate an initial prediction with several layers of temporal
convolutions and refine these predictions over multiple stages, also with
temporal convolutions. Although these approaches capture long-term temporal
patterns, the initial predictions do not adequately consider the spatial
hierarchy among the human joints. To address this limitation, we present
multi-stage spatial-temporal graph convolutional neural networks (MS-GCN). Our
framework decouples the architecture of the initial prediction generation stage
from the refinement stages. Specifically, we replace the initial stage of
temporal convolutions with spatial-temporal graph convolutions, which better
exploit the spatial configuration of the joints and their temporal dynamics.
Our framework was compared to four strong baselines on five tasks. Experimental
results demonstrate that our framework achieves state-of-the-art performance.

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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org