  Self-supervised representation learning for human action recognition has
developed rapidly in recent years. Most of the existing works are based on
skeleton data while using a multi-modality setup. These works overlooked the
differences in performance among modalities, which led to the propagation of
erroneous knowledge between modalities while only three fundamental modalities,
i.e., joints, bones, and motions are used, hence no additional modalities are
explored.
  In this work, we first propose an Implicit Knowledge Exchange Module (IKEM)
which alleviates the propagation of erroneous knowledge between low-performance
modalities. Then, we further propose three new modalities to enrich the
complementary information between modalities. Finally, to maintain efficiency
when introducing new modalities, we propose a novel teacher-student framework
to distill the knowledge from the secondary modalities into the mandatory
modalities considering the relationship constrained by anchors, positives, and
negatives, named relational cross-modality knowledge distillation. The
experimental results demonstrate the effectiveness of our approach, unlocking
the efficient use of skeleton-based multi-modality data. Source code will be
made publicly available at https://github.com/desehuileng0o0/IKEM.
