| ---
|
| 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}
|
| }
|
| ```
|
|
|
