Domain generalization (DG) seeks robust Vision Transformer (ViT) performance on unseen domains. Efficiently adapting pretrained ViTs for DG is challenging; standard fine-tuning is costly and can impair generalization. We propose GNN-MoE, enhancing Parameter-Efficient Fine-Tuning (PEFT) for DG with a Mixture-of-Experts (MoE) framework using efficient Kronecker adapters. Instead of token-based routing, a novel Graph Neural Network (GNN) router (GCN, GAT, SAGE) operates on inter-patch graphs to dynamically assign patches to specialized experts. This context-aware GNN routing leverages inter-patch relationships for better adaptation to domain shifts. GNN-MoE achieves state-of-the-art or competitive DG benchmark performance with high parameter efficiency, highlighting the utility of graph-based contextual routing for robust, lightweight DG.
@article{arxiv.2511.04008,
title = {GNN-MoE: Context-Aware Patch Routing using GNNs for Parameter-Efficient Domain Generalization},
author = {Mahmoud Soliman and Omar Abdelaziz and Ahmed Radwan and Anand and Mohamed Shehata},
journal= {arXiv preprint arXiv:2511.04008},
year = {2025}
}