  Hypergraphs are a generalized data structure of graphs to model higher-order
correlations among entities, which have been successfully adopted into various
research domains. Meanwhile, HyperGraph Neural Network (HGNN) is currently the
de-facto method for hypergraph representation learning. However, HGNN aims at
single hypergraph learning and uses a pre-concatenation approach when
confronting multi-modal datasets, which leads to sub-optimal exploitation of
the inter-correlations of multi-modal hypergraphs. HGNN also suffers the
over-smoothing issue, that is, its performance drops significantly when layers
are stacked up. To resolve these issues, we propose the Residual enhanced
Multi-Hypergraph Neural Network, which can not only fuse multi-modal
information from each hypergraph effectively, but also circumvent the
over-smoothing issue associated with HGNN. We conduct experiments on two 3D
benchmarks, the NTU and the ModelNet40 datasets, and compare against multiple
state-of-the-art methods. Experimental results demonstrate that both the
residual hypergraph convolutions and the multi-fusion architecture can improve
the performance of the base model and the combined model achieves a new
state-of-the-art. Code is available at
\url{https://github.com/OneForward/ResMHGNN}.
