  One essential problem in skeleton-based action recognition is how to extract
discriminative features over all skeleton joints. However, the complexity of
the recent State-Of-The-Art (SOTA) models for this task tends to be exceedingly
sophisticated and over-parameterized. The low efficiency in model training and
inference has increased the validation costs of model architectures in
large-scale datasets. To address the above issue, recent advanced separable
convolutional layers are embedded into an early fused Multiple Input Branches
(MIB) network, constructing an efficient Graph Convolutional Network (GCN)
baseline for skeleton-based action recognition. In addition, based on such the
baseline, we design a compound scaling strategy to expand the model's width and
depth synchronously, and eventually obtain a family of efficient GCN baselines
with high accuracies and small amounts of trainable parameters, termed
EfficientGCN-Bx, where "x" denotes the scaling coefficient. On two large-scale
datasets, i.e., NTU RGB+D 60 and 120, the proposed EfficientGCN-B4 baseline
outperforms other SOTA methods, e.g., achieving 91.7% accuracy on the
cross-subject benchmark of NTU 60 dataset, while being 3.15x smaller and 3.21x
faster than MS-G3D, which is one of the best SOTA methods. The source code in
PyTorch version and the pretrained models are available at
https://github.com/yfsong0709/EfficientGCNv1.
