Relationship-Aware Primal-Dual Graph Attention Network For Scene Graph Generation

摘要

The relationships and interactions between objects contain rich semantic information, which plays a crucial role in scene understanding. Existing methods do not attach great importance to the expression of relational features. To tackle this problem, we propose a novel Relationship-aware Primal-Dual Graph Attention Network (RPDGAT) to extract the comprehensive semantic features of objects and explore the sparse graph inference for scene graph generation. RPDGAT mines the inherent attributes and the relationships between objects by fusing multiple features, e.g. appearance, spatial, and category features. After feature extraction, we design a trainable relationship distance measure network to construct the robust and sparse graph structure for efficient graphical message passing. Moreover, it can preserve the contextual cues and neighboring dependency for objects and relationships from the interaction between primal and dual graphs. Extensive experimental results present the improved performance of our method over several state-of-the-art methods on the visual genome datasets.