DA-GCN: Deformable Adaptive - Graph Convolutional Network for Skeleton-Based Action Recognition

This paper proposes a Deformable Adaptive Graph Convolutional Network (DA-GCN) to address the challenge of capturing discriminative joint features in skeleton-based action recognition. By projecting joint features into high-dimensional space via linear transformations, the framework enhances distinguishability of semantically similar joints and enables precise Top-K sampling, while dynamically integrating static anatomical topology with adaptive similarity weights to construct computationally efficient spatiotemporal graphs. An innovative Adaptive Alpha Learning (A2L) module optimizes fusion weights for joint, bone, and velocity features at the decision level. Extensive experiments on NTU RGB+D and NTU RGB+D 120 datasets demonstrate state-of-the-art performance with 7.16M parameters-achieving 93.5% and 90.63% cross-subject accuracy and surpassing the best baseline by 0.4% and 0.3%, establishing a new paradigm for fine-grained action understanding.