Research map / Label-efficient and robust learning / Domain adaptation and generalization
From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the full coarse-to-fine adaptation method instead of CC-SSL | Animal pose estimation · TigDog horse | PCK@0.05 accuracy (Mean): 70.77 -> 79.50 | Helped |
| Use the full coarse-to-fine adaptation method instead of CC-SSL | Animal pose estimation · TigDog tiger | PCK@0.05 accuracy (Mean): 64.14 -> 67.76 | Helped |
| Use the full method rather than the real-data supervised model | Animal pose estimation · TigDog horse | PCK@0.05 accuracy (Mean): 78.98 -> 79.50 | Helped |
| Use the full method rather than the real-data supervised model | Animal pose estimation · TigDog tiger | PCK@0.05 accuracy (Mean): 81.99 -> 67.76 | Hurt |
| Use the full method instead of CC-SSL | Animal pose estimation · VisDA2019 horse, Painting | PCK@0.05 accuracy (Visible Kpts): 73.71 -> 80.05 | Helped |
| Use the full method instead of CC-SSL | Animal pose estimation · VisDA2019 horse, Painting | PCK@0.05 accuracy (Full Kpts): 71.56 -> 78.42 | Helped |
| Use the full method instead of CC-SSL | Animal pose estimation · Zebra | PCK@0.05 accuracy (Mean): 50.07 -> 57.23 | Helped |
| Use the full method instead of CC-SSL | Animal pose estimation · Animal-Pose | PCK@0.05 accuracy (Mean): 47.60 -> 56.77 | Helped |
| Train MDAM with pseudo labels only rather than CC-SSL (ablation) | MDAM · TigDog | PCK@0.05 accuracy (Mean): 67.52 -> 69.69 | Helped |
| Use teacher supervision alone instead of pseudo-label supervision (ablation) | MDAM · TigDog | PCK@0.05 accuracy (Mean): 69.69 -> 68.70 | Hurt |
| Add progressive outer-loop teacher pseudo-label updates (ablation) | MDAM with teacher supervision · TigDog | PCK@0.05 accuracy (Mean): 68.70 -> 72.70 | Helped |
| Add the inner self-distillation coarse-update loop (ablation) | MDAM with outer-loop updates · TigDog | PCK@0.05 accuracy (Mean): 72.70 -> 73.25 | Helped |
Closest papers by meaning
- ScarceNet: Animal Pose Estimation With Scarce Annotations (CVPR 2023 · similarity 0.92)
- Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation (ICLR 2024 · similarity 0.85)
- Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs (NeurIPS 2026 · similarity 0.85)
- Probabilistic Prompt Distribution Learning for Animal Pose Estimation (CVPR 2025 · similarity 0.85)
- Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation (CVPR 2024 · similarity 0.84)
- Fast Adaptation for Human Pose Estimation via Meta-Optimization (CVPR 2024 · similarity 0.84)
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