  Recently, the significant achievements have been made in skeleton-based human
action recognition with the emergence of graph convolutional networks (GCNs).
However, the state-of-the-art (SOTA) models used for this task focus on
constructing more complex higher-order connections between joint nodes to
describe skeleton information, which leads to complex inference processes and
high computational costs. To address the slow inference speed caused by overly
complex model structures, we introduce re-parameterization and
over-parameterization techniques to GCNs and propose two novel high-performance
inference GCNs, namely HPI-GCN-RP and HPI-GCN-OP. After the completion of model
training, model parameters are fixed. HPI-GCN-RP adopts re-parameterization
technique to transform high-performance training model into fast inference
model through linear transformations, which achieves a higher inference speed
with competitive model performance. HPI-GCN-OP further utilizes
over-parameterization technique to achieve higher performance improvement by
introducing additional inference parameters, albeit with slightly decreased
inference speed. The experimental results on the two skeleton-based action
recognition datasets demonstrate the effectiveness of our approach. Our
HPI-GCN-OP achieves performance comparable to the current SOTA models, with
inference speeds five times faster. Specifically, our HPI-GCN-OP achieves an
accuracy of 93\% on the cross-subject split of the NTU-RGB+D 60 dataset, and
90.1\% on the cross-subject benchmark of the NTU-RGB+D 120 dataset. Code is
available at github.com/lizaowo/HPI-GCN.
