The authors study multimodal graph neural networks and find that decoupled architectures (which separate feature propagation from model training) are much more efficient than tightly coupled ones, but suffer from "modal conflict"—cross-modal semantic divergence during propagation and misalignment during aggregation. They propose CAMPA, which injects cross-modal similarity into message passing and uses trajectory-level attention to align features across modalities and propagation hops. Experiments show CAMPA outperforms both coupled and decoupled baselines while staying efficient.
Main takeaways:
- Decoupled graph neural networks (which pre-propagate features separately from training) are faster and more scalable than coupled architectures.
- The bottleneck is modal conflict: independent diffusion causes semantic drift across modalities, and naive fusion fails to align multi-hop feature trajectories.
- CAMPA fixes this with two-stage alignment: cross-modal similarity priors during propagation and trajectory-level self/cross-attention during aggregation.
- The method preserves the efficiency of decoupled architectures while consistently improving performance on diverse benchmarks.
- The approach handles long-range dependencies across both modalities and propagation hops.