Research map / Graph learning / Graph algorithms and clustering
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KLj/r or KLj*r instead of the published branch-and-cut method | Human pose estimation · MPII Human Pose, |V|=150 | AP: 65.5 -> 66.5 | Helped |
| Increase graph size from |V|=150 to |V|=420 | KLj/r and KLj*r · MPII Human Pose | AP: 66.5 -> 70.6; 66.6 -> 70.6 | Helped |
| Jointly update decomposition and node labeling (KLj*r instead of KLj/r) | NL-LMP local search · MPII Human Pose, |V|=150 | AP: 66.5 -> 66.6 | Helped |
| Jointly update decomposition and node labeling (KLj*r instead of KLj/r) | NL-LMP local search · KITTI validation | AP: 50.5 -> 50.3 | Mixed |
| Apply component fusion post-processing (ablation) | KLj/r instance segmentation · KITTI validation | AP: 43.0 -> 50.5 | Helped |
| Apply component fusion post-processing (ablation) | KLj*r instance segmentation · KITTI validation | AP: 43.5 -> 50.3 | Helped |
| Use ResNet semantic unary scores instead of Uhrig semantic scores (ablation) | Cityscapes instance segmentation · Cityscapes validation | AP: 9.4 -> 11.4 | Helped |
| Use KLj/r or KLj*r to solve the joint graph problem instead of the published method | Multiple object tracking · MOT benchmark test | MOTA: 46.3 -> 47.6 | Helped |
| Use KLj/r instead of KLj*r | NL-LMP local search · Multiple object tracking | Hz: 0.7 -> 8.3 | Helped |
| Replace KLj/r with KLj*r, which jointly transforms decomposition and labels | NL-LMP local search · Multiple object tracking | MOTA: 47.6 -> 47.6 | No clear effect |
Closest papers by meaning
- DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation (CVPR 2016 · similarity 0.86)
- Bridge the Points: Graph-based Few-shot Segment Anything Semantically (NeurIPS 2024 · similarity 0.84)
- COMPOSE: Hypergraph Cover Optimization for Multi-view 3D Human Pose Estimation (NeurIPS 2026 · similarity 0.84)
- Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging (CVPR 2025 · similarity 0.84)
- GRPose: Learning Graph Relations for Human Image Generation with Pose Priors (AAAI 2025 · similarity 0.83)
- Learning to Filter Outlier Edges in Global SfM (CVPR 2025 · similarity 0.83)
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