Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition

17 Mar 2020Jongmin YuYongsang YoonMoongu Jeon

In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have achieved remarkable performance recently. However, current state-of-the-art methods for skeleton-based action recognition usually work on the assumption that the completely observed skeletons will be provided... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Skeleton Based Action Recognition Kinetics-Skeleton dataset PeGCN Accuracy 34.8 # 10
Skeleton Based Action Recognition NTU RGB+D PeGCN Accuracy (CV) 93.4 # 19
Accuracy (CS) 85.6 # 28

Methods used in the Paper


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