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Hierarchical Action Classification with Network Pruning
Metrics concerned:Accuracy
Research on human action classification has made significant progresses in
the past few years. Most deep learning methods focus on improving performance
by adding more network components. We propose, however, to better utilize
auxiliary mechanisms, including hierarchical classification, network pruning,
and skeleton-based preprocessing, to boost the model robustness and
performance. We test the effectiveness of our method on four commonly used
testing datasets: NTU RGB+D 60, NTU RGB+D 120, Northwestern-UCLA Multiview
Action 3D, and UTD Multimodal Human Action Dataset. Our experiments show that
our method can achieve either comparable or better performance on all four
datasets. In particular, our method sets up a new baseline for NTU 120, the
largest dataset among the four. We also analyze our method with extensive
comparisons and ablation studies.
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