  Recent progresses on self-supervised 3D human action representation learning
are largely attributed to contrastive learning. However, in conventional
contrastive frameworks, the rich complementarity between different skeleton
modalities remains under-explored. Moreover, optimized with distinguishing
self-augmented samples, models struggle with numerous similar positive
instances in the case of limited action categories. In this work, we tackle the
aforementioned problems by introducing a general Inter- and Intra-modal Mutual
Distillation (I$^2$MD) framework. In I$^2$MD, we first re-formulate the
cross-modal interaction as a Cross-modal Mutual Distillation (CMD) process.
Different from existing distillation solutions that transfer the knowledge of a
pre-trained and fixed teacher to the student, in CMD, the knowledge is
continuously updated and bidirectionally distilled between modalities during
pre-training. To alleviate the interference of similar samples and exploit
their underlying contexts, we further design the Intra-modal Mutual
Distillation (IMD) strategy, In IMD, the Dynamic Neighbors Aggregation (DNA)
mechanism is first introduced, where an additional cluster-level discrimination
branch is instantiated in each modality. It adaptively aggregates
highly-correlated neighboring features, forming local cluster-level
contrasting. Mutual distillation is then performed between the two branches for
cross-level knowledge exchange. Extensive experiments on three datasets show
that our approach sets a series of new records.
