| license: mit | |
| library_name: pytorch | |
| tags: | |
| - digital-twins | |
| - scene-graphs | |
| - robotics | |
| - computer-vision | |
| - graph-neural-network | |
| # CF-SupportNet | |
| CF-SupportNet is the learned edge-scoring component of **MedPhyGraph** | |
| (ECCV 2026 TwinWorld Workshop). It is NOT the full MedPhyGraph | |
| framework by itself: MedPhyGraph also applies deterministic State | |
| Consistency and Union-Based Transition-Aware Consistency (non-learned, | |
| code-only) on top of these scores to produce the final maintained | |
| support graph. See the paper and code at | |
| https://medphygraph.github.io/ for the complete pipeline. | |
| ## Files | |
| - `health_dyphygraph_r1.0_seed0.pt` — primary checkpoint used for all | |
| headline paper results (rho=1.0, seed 0) | |
| - `health_dyphygraph_r1.0_seed{1-4}.pt` — used only for the paper's | |
| multi-seed reproducibility audit; not intended as alternative | |
| production checkpoints | |
| ## Architecture | |
| One-layer GRU (hidden width 64), fusion MLP, 25,409 trainable | |
| parameters. Full training details in the paper, Section 3.4. | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{gholizadeh2026medphygraph, | |
| title={MedPhyGraph: Counterfactual Support-Graph Maintenance for Dynamic Built-Environment Digital Twins}, | |
| author={Gholizadeh HamlAbadi, Kamran and Vahdati, Monica and El Saddik, Abdulmotaleb}, | |
| booktitle={ECCV 2026 TwinWorld Workshop}, | |
| year={2026} | |
| } | |
| ``` | |