Research map / Probabilistic and scientific modeling / Molecules and proteins
Global Matching With Overlapping Attention for Optical Flow Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add global matching before the RAFT optimization | RAFT · Sintel train Clean and KITTI-15 train | AEPE / F1-epe: 1.43 -> 1.26; 5.04 -> 4.74 | Helped |
| Add global matching to GMFlowNet (ablation) | GMFlowNet · Sintel train Clean and Final; KITTI-15 train | AEPE / F1-epe: 1.24 -> 1.14; 2.82 -> 2.71; 4.58 -> 4.24 | Helped |
| Use patch-based overlapping attention rather than per-pixel attention (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.32 -> 1.14 | Helped |
| Use 3-Convs for initial features rather than no initial feature extractor (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.31 -> 1.14 | Helped |
| Use 3-Convs for initial features rather than ResNet (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.27 -> 1.14 | Helped |
| Replace POLA with ResNet context features (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.14 -> 1.26 | Hurt |
| Replace POLA with Swin Transformer attention (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.14 -> 1.33 | Hurt |
| Replace POLA with ViT (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.14 -> 1.33 | Hurt |
| Reduce attention blocks from 6 to 3 (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.14 -> 1.27 | Hurt |
| Increase attention blocks from 6 to 12 (ablation) | GMFlowNet · Sintel train Clean | AEPE: 1.14 -> 1.12 | Helped |
| Use 6 rather than 12 attention blocks (ablation) | GMFlowNet · KITTI-15 train | F1-all: 15.6 -> 15.4 | Mixed |
| Use 3-Convs rather than no initial feature extractor (ablation) | GMFlowNet · Sintel train Final | AEPE: 2.83 -> 2.71 | Hurt |
Closest papers by meaning
- A Theory on Flow Matching with Neural Networks (NeurIPS 2026 · similarity 0.88)
- Sparse Global Matching for Video Frame Interpolation with Large Motion (CVPR 2024 · similarity 0.88)
- Gaussian Mixture Flow Matching Models (ICML 2025 · similarity 0.86)
- Value Gradient Guidance for Flow Matching Alignment (NeurIPS 2025 · similarity 0.86)
- EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation (CVPR 2025 · similarity 0.86)
- Blockwise Flow Matching: Improving Flow Matching Models For Efficient High-Quality Generation (NeurIPS 2025 · similarity 0.86)
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