Research map / Detection and segmentation / Tracking
Learning a Neural Solver for Multiple Object Tracking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use time-aware instead of vanilla node updates (ablation) | Neural Solver · MOT17 validation | IDF1: 67.3 -> 70.0 | Helped |
| Use time-aware instead of vanilla node updates (ablation) | Neural Solver · MOT17 validation | MOTA: 63.0 -> 64.0 | Helped |
| Use time-aware instead of vanilla node updates (ablation) | Neural Solver · MOT17 validation | constraint satisfaction: 82.1 -> 98.8 | Helped |
| Add relative-position edge features to time-difference features (ablation) | Neural Solver · MOT17 validation | MOTA: 58.8 -> 63.6 | Helped |
| Add CNN appearance-distance features to time and relative-position features (ablation) | Neural Solver · MOT17 validation | IDF1: 68.7 -> 70.0 | Helped |
| Add CNN appearance-distance features to time and relative-position features (ablation) | Neural Solver · MOT17 validation | MOTA: 63.6 -> 64.0 | Helped |
| Use greedy rather than exact LP rounding (ablation) | Neural Solver · MOT17 validation | IDF1: 70.0 -> 69.8 | Hurt |
Closest papers by meaning
- Learnable Graph Matching: Incorporating Graph Partitioning With Deep Feature Learning for Multiple Object Tracking (CVPR 2021 · similarity 0.91)
- GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning (CVPR 2020 · similarity 0.90)
- Deep Network Flow for Multi-Object Tracking (CVPR 2017 · similarity 0.90)
- Multiple Object Tracking as ID Prediction (CVPR 2025 · similarity 0.89)
- Multi-Scene Generalized Trajectory Global Graph Solver with Composite Nodes for Multiple Object Tracking (AAAI 2024 · similarity 0.88)
- DiffusionTrack: Diffusion Model for Multi-Object Tracking (AAAI 2024 · similarity 0.87)
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