Research map / Graph learning / GNN architectures
Adaptive GNN for Image Analysis and Editing
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| QIA-GNN instead of Korshunova's method | QIA-GNN · Face swapping | GMSD: 0.2874 -> 0.0890 | Helped |
| QIA-GNN instead of Kemelmacher-Shlizerman | QIA-GNN · Transfiguring | GMSD: 0.2114 -> 0.0909 | Helped |
| QIA-GNN instead of Nirkin | QIA-GNN · Transfiguring | GMSD: 0.1472 -> 0.0909 | Helped |
| QIA-GNN instead of CVC | QIA-GNN · Low-light enhancement Img1 | discrete entropy: 6.54 -> 7.48 | Helped |
| QIA-GNN instead of CVC | QIA-GNN · Low-light enhancement Img5 | discrete entropy: 4.27 -> 6.04 | Helped |
| QIA-GNN instead of LIME | QIA-GNN · Low-light enhancement Img6 | discrete entropy: 7.23 -> 7.71 | Helped |
| QIA-GNN instead of LIME | QIA-GNN · Low-light enhancement Img1 | discrete entropy: 7.67 -> 7.48 | Hurt |
| QIA-GNN instead of LIME | QIA-GNN · Low-light enhancement Img2 | discrete entropy: 7.43 -> 7.47 | No clear effect |
| QIA-GNN instead of LIME | QIA-GNN · Low-light enhancement Img4 | discrete entropy: 7.65 -> 7.65 | No clear effect |
Closest papers by meaning
- Improving Graph Neural Networks with Learnable Propagation Operators (ICML 2023 · similarity 0.86)
- The Intelligible and Effective Graph Neural Additive Network (NeurIPS 2024 · similarity 0.85)
- Adaptive Kernel Graph Neural Network (AAAI 2022 · similarity 0.85)
- Adaptive Diffusion in Graph Neural Networks (NeurIPS 2021 · similarity 0.85)
- Generalizing CNNs to graphs with learnable neighborhood quantization (NeurIPS 2024 · similarity 0.85)
- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.