GNN-MoE: Context-Aware Patch Routing using GNNs for Parameter-Efficient Domain Generalization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912690165776384 |
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| author | Soliman, Mahmoud Abdelaziz, Omar Radwan, Ahmed Anand Shehata, Mohamed |
| author_facet | Soliman, Mahmoud Abdelaziz, Omar Radwan, Ahmed Anand Shehata, Mohamed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GNN-MoE: Context-Aware Patch Routing using GNNs for Parameter-Efficient Domain Generalization Soliman, Mahmoud Abdelaziz, Omar Radwan, Ahmed Anand Shehata, Mohamed Computer Vision and Pattern Recognition 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. |
| title | GNN-MoE: Context-Aware Patch Routing using GNNs for Parameter-Efficient Domain Generalization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.04008 |