Research map / 3D vision / Human pose estimation
Structured Feature Learning for Pose Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add single upward-direction feature-level message passing (ablation) | VGG-16 pose baseline · FLIC test | mean strict PCP: 75.1 -> 87.9 | Helped |
| Add bidirectional tree message passing instead of single-direction passing (ablation) | Structured Feature Learning · FLIC test | mean strict PCP: 87.9 -> 93.4 | Helped |
| Add multiple appearance-mixture score maps per joint (ablation) | Bi-direct · FLIC test | mean strict PCP: 93.4 -> 95.2 | Helped |
| Add structured feature learning with bidirectional message passing and multiple score maps (ablation) | VGG-16 pose baseline · FLIC test | mean strict PCP: 75.1 -> 95.2 | Helped |
| Use structured feature learning instead of the strongest reported baseline | Pose estimation · FLIC test | mean strict PCP: 91.9 -> 95.2 | Helped |
| Use structured feature learning instead of the strongest reported baseline | Pose estimation · LSP test | mean strict PCP: 75.0 -> 80.8 | Helped |
Closest papers by meaning
- Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos (CVPR 2017 · similarity 0.87)
- Does Learning Specific Features for Related Parts Help Human Pose Estimation? (CVPR 2019 · similarity 0.87)
- End-To-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation (CVPR 2016 · similarity 0.87)
- 3D Human Pose Estimation From a Single Image via Distance Matrix Regression (CVPR 2017 · similarity 0.87)
- PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation (CVPR 2023 · similarity 0.87)
- Sequential Joint Dependency Aware Human Pose Estimation with State Space Model (AAAI 2025 · similarity 0.85)
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