  Graph Convolutional Networks (GCNs) have long defined the state-of-the-art in
skeleton-based action recognition, leveraging their ability to unravel the
complex dynamics of human joint topology through the graph's adjacency matrix.
However, an inherent flaw has come to light in these cutting-edge models: they
tend to optimize the adjacency matrix jointly with the model weights. This
process, while seemingly efficient, causes a gradual decay of bone connectivity
data, culminating in a model indifferent to the very topology it sought to map.
As a remedy, we propose a threefold strategy: (1) We forge an innovative
pathway that encodes bone connectivity by harnessing the power of graph
distances. This approach preserves the vital topological nuances often lost in
conventional GCNs. (2) We highlight an oft-overlooked feature - the temporal
mean of a skeletal sequence, which, despite its modest guise, carries highly
action-specific information. (3) Our investigation revealed strong variations
in joint-to-joint relationships across different actions. This finding exposes
the limitations of a single adjacency matrix in capturing the variations of
relational configurations emblematic of human movement, which we remedy by
proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC.
This evolution slashes parameters by a substantial margin (above 40%), while
elevating performance beyond original GCNs. Our full model, the BlockGCN,
establishes new standards in skeleton-based action recognition for small model
sizes. Its high accuracy, notably on the large-scale NTU RGB+D 120 dataset,
stand as compelling proof of the efficacy of BlockGCN.
