  Existing research on action recognition treats activities as monolithic
events occurring in videos. Recently, the benefits of formulating actions as a
combination of atomic-actions have shown promise in improving action
understanding with the emergence of datasets containing such annotations,
allowing us to learn representations capturing this information. However, there
remains a lack of studies that extend action composition and leverage multiple
viewpoints and multiple modalities of data for representation learning. To
promote research in this direction, we introduce Home Action Genome (HOMAGE): a
multi-view action dataset with multiple modalities and view-points supplemented
with hierarchical activity and atomic action labels together with dense scene
composition labels. Leveraging rich multi-modal and multi-view settings, we
propose Cooperative Compositional Action Understanding (CCAU), a cooperative
learning framework for hierarchical action recognition that is aware of
compositional action elements. CCAU shows consistent performance improvements
across all modalities. Furthermore, we demonstrate the utility of co-learning
compositions in few-shot action recognition by achieving 28.6% mAP with just a
single sample.
