InfoGCN++: Learning Representation by Predicting the Future for Online
  Human Skeleton-based Action Recognition
Seunggeun Chi Hyung-gun Chi Qixing Huang Karthik Ramani
Skeleton-based action recognition has made significant advancements recently,
with models like InfoGCN showcasing remarkable accuracy. However, these models
exhibit a key limitation: they necessitate complete action observation prior to
classification, which constrains their applicability in real-time situations
such as surveillance and robotic systems. To overcome this barrier, we
introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed
for online skeleton-based action recognition. InfoGCN++ augments the abilities
of the original InfoGCN model by allowing real-time categorization of action
types, independent of the observation sequence's length. It transcends
conventional approaches by learning from current and anticipated future
movements, thereby creating a more thorough representation of the entire
sequence. Our approach to prediction is managed as an extrapolation issue,
grounded on observed actions. To enable this, InfoGCN++ incorporates Neural
Ordinary Differential Equations, a concept that lets it effectively model the
continuous evolution of hidden states. Following rigorous evaluations on three
skeleton-based action recognition benchmarks, InfoGCN++ demonstrates
exceptional performance in online action recognition. It consistently equals or
exceeds existing techniques, highlighting its significant potential to reshape
the landscape of real-time action recognition applications. Consequently, this
work represents a major leap forward from InfoGCN, pushing the limits of what's
possible in online, skeleton-based action recognition. The code for InfoGCN++
is publicly available at https://github.com/stnoah1/infogcn2 for further
exploration and validation.