Research map / Probabilistic and scientific modeling / Molecules and proteins
Inference in Higher Order MRF-MAP Problems With Small and Large Cliques
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the hybrid flow-based and polyhedral inference framework instead of SOSMNP | MRF-MAP inference with mixed small and large cliques · Synthetic count-based cliques, 50×50 image; small clique 2×1 | runtime (seconds): 274.27 -> 11.54 | Helped |
| Use the hybrid flow-based and polyhedral inference framework instead of SOSMNP | MRF-MAP inference with mixed small and large cliques · Synthetic edge-based cliques, 50×50 image; small clique 2×1 | runtime (seconds): 250.67 -> 17.41 | Helped |
| Use the hybrid flow-based and polyhedral inference framework instead of SOSMNP | MRF-MAP inference with mixed small and large cliques · Synthetic count-based cliques; 2500 pixels, 40 big cliques | runtime (seconds): 295.73 -> 31.28 | Helped |
| Use the hybrid flow-based and polyhedral inference framework instead of SOSMNP | MRF-MAP inference with mixed small and large cliques · Synthetic count-based cliques; 6400 pixels, 50 big cliques | runtime (seconds): 349.71 -> 32.50 | Helped |
| Increase the small-clique window size from 2×2 to 4×3 | HybridMNGC · Synthetic count-based cliques, 50×50 image; 25 big cliques | runtime (seconds): 9.42 -> 58.48 | Hurt |
| Increase the number of big cliques from 40 to 120 | HybridMNGC · Synthetic count-based cliques; 2500 pixels | runtime (seconds): 31.28 -> 94.66 | Hurt |
| Increase image size from 2500 to 6400 pixels | HybridMNGC · Synthetic count-based cliques; 50 big cliques | runtime (seconds): 34.69 -> 32.50 | Mixed |
| Combine small pairwise cliques with large region-level cliques | HybridMNGC object segmentation · Pascal VOC object segmentation with Gaussian noise | segmentation quality | Helped |
Closest papers by meaning
- Min Norm Point Algorithm for Higher Order MRF-MAP Inference (CVPR 2016 · similarity 0.93)
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- Efficient semidefinite-programming-based inference for binary and multi-class MRFs (NeurIPS 2020 · similarity 0.88)
- MFP: Making Full Use of Probability Maps for Interactive Image Segmentation (CVPR 2024 · similarity 0.83)
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