Research map / Learning theory and algorithms / Combinatorial and search optimization
Relaxation-Based Preprocessing Techniques for Markov Random Field Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add PR-based preprocessing to α-expansion | α-expansion · Vision benchmarks | speedup: 1.5x-12x | Helped |
| Replace DEE with the PR 1-condition (ablation) | α-expansion on Color-Seg-n4 · Color-Seg-n4 | partial persistent variables: 14% more | Helped |
| Use approximate 2-condition relaxation instead of the 1-condition (ablation) | iPR on Color-Seg-n4 · Color-Seg-n4 | partial persistent variables: additional 10% more | Helped |
| Use approximate 3-condition relaxation instead of the approximate 2-condition (ablation) | iPR on Color-Seg-n4 · Color-Seg-n4 | partial persistent variables | Helped |
| Increase the stopping parameter τ (ablation) | DEE and PR preprocessing · Color-Seg-n4 | persistent variable ratio | Helped |
| Increase the stopping parameter τ (ablation) | DEE and PR preprocessing · Color-Seg-n4 | overall running time | Mixed |
| Apply Kovtun preprocessing directly to the multi-label Middlebury problem | α-expansion · Middlebury MRF dataset | running time: at least 5x slower | Hurt |
Closest papers by meaning
- Efficient semidefinite-programming-based inference for binary and multi-class MRFs (NeurIPS 2020 · similarity 0.90)
- Memory Efficient Max Flow for Multi-Label Submodular MRFs (CVPR 2016 · similarity 0.90)
- Continuous Relaxation of MAP Inference: A Nonconvex Perspective (CVPR 2018 · similarity 0.89)
- Scalable Inference of Sparsely-changing Gaussian Markov Random Fields (NeurIPS 2021 · similarity 0.88)
- Lifted Hinge-Loss Markov Random Fields (AAAI 2019 · similarity 0.87)
- Learning Juntas under Markov Random Fields (NeurIPS 2025 · similarity 0.85)
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