Self-Supervised 3-D Action Recognition by Contrasting Context-Enhanced Action Embeddings

3-D action recognition become a fast-pacing field in recent years. However, traditional approaches have limitations. They either focus on modeling overly detailed yet redundant information by reconstructing the coordinates of each body joint. Alternatively, they treat actions as a whole, overlooking the spatial and temporal variations in the semantic locality of actions. To address these limitations, we propose representing long-term actions as contexts of short-term actions organized by locality-aware graphs. In our framework, we take the inspiration that the continuity of motion and pose variations generate higher correlations. These correlations occur among spatio-temporally adjacent joints. Built upon this, we craft short-term actions as embeddings using spatio-temporal graph convolutions. This graph-based encoding not only captures richer high-level semantics but also maintains an awareness of the topology. To capture long-term action dynamics effectively, we integrate a graph convolutional gated recurrent unit (GraphGRU) for the fusion of action embeddings. Additionally, we introduce the context-aware topological attention (CTA) mechanism. Positioned between embedding encoding and context aggregation phases, CTA amplifies the features of context-relevant nodes. Lastly, we create self-supervision by contrasting predicted embeddings with actual encoded embeddings. This approach explicitly learns changes in dynamics to obtain distinct embeddings. Empirical evaluations demonstrate that our approach outperforms mainstream unsupervised 3-D action recognition methods.