Research map / Label-efficient and robust learning / Recognition and long-tail
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PatchMatch-based neighborhood consensus with the learned local scoring function | ResNet-101 semantic correspondence · PF-PASCAL | PCK α=0.1: 85.9 -> 90.6 | Helped |
| Use PatchMatch-based neighborhood consensus with the learned local scoring function | ResNet-101 semantic correspondence · SPAIR-71K, trained/fine-tuned | PCK α=0.1: 35.6 -> 50.4 | Helped |
| Use PMNCfast configuration rather than NC-Net | semantic correspondence inference · PF-PASCAL | inference time: 0.29 -> 0.09 | Helped |
| Use PMNCfast configuration rather than ANC-Net | semantic correspondence inference · PF-PASCAL | memory: 1,310 -> 273 | Helped |
| Add PatchMatch refinement iterations after greedy matching (ablation) | PMNC · PF-PASCAL | PCK α=0.05: 77.9 -> 82.4 | Helped |
| Use 3 rather than 2 PatchMatch iterations (ablation) | PMNC · PF-PASCAL | PCK α=0.05: 82.4 -> 81.9 | Hurt |
| Use 4 rather than 2 PatchMatch iterations (ablation) | PMNC · PF-PASCAL | PCK α=0.05: 82.4 -> 82.1 | Hurt |
| Set probability-map σ to 0.15 rather than 0.60 (ablation) | PMNC training · PF-PASCAL | PCK α=0.05: 82.4 -> 80.9 | Hurt |
| Set probability-map σ to 0.30 rather than 0.60 (ablation) | PMNC training · PF-PASCAL | PCK α=0.05: 82.4 -> 80.1 | Hurt |
| Set probability-map σ to 0.45 rather than 0.60 (ablation) | PMNC training · PF-PASCAL | PCK α=0.05: 82.4 -> 80.1 | Hurt |
| Set probability-map σ to 0.75 rather than 0.60 (ablation) | PMNC training · PF-PASCAL | PCK α=0.05: 82.4 -> 80.6 | Hurt |
| Set probability-map σ to 0.90 rather than 0.60 (ablation) | PMNC training · PF-PASCAL | PCK α=0.05: 82.4 -> 78.4 | Hurt |
Closest papers by meaning
- Neighbourhood Consensus Networks (NeurIPS 2018 · similarity 0.90)
- SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching (CVPR 2024 · similarity 0.87)
- Near, far: Patch-ordering enhances vision foundation models' scene understanding (ICLR 2025 · similarity 0.87)
- Diffusion Model for Dense Matching (ICLR 2024 · similarity 0.86)
- Locality Alignment Improves Vision-Language Models (ICLR 2025 · similarity 0.86)
- Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence (NeurIPS 2025 · similarity 0.86)
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