Papers › Pretraining boosts out-of-domain robustness for pose estimation
Pretraining boosts out-of-domain robustness for pose estimation
Alexander Mathis, Thomas Biasi, Steffen Schneider, Mert Yüksekgönül, Byron Rogers, Matthias Bethge, Mackenzie W. Mathis
Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe the generalization ability with three architecture classes (MobileNetV2s, ResNets, and EfficientNets) for pose estimation. We developed a dataset of 30 horses that allowed for both "within-domain" and "out-of-domain" (unseen horse) benchmarking - this is a crucial test for robustness that current human pose estimation benchmarks do not directly address. We show that better ImageNet-performing architectures perform better on both within- and out-of-domain data if they are first pretrained on ImageNet. We additionally show that better ImageNet models generalize better across animal species. Furthermore, we introduce Horse-C, a new benchmark for common corruptions for pose estimation, and confirm that pretraining increases performance in this domain shift context as well. Overall, our results demonstrate that transfer learning is beneficial for out-of-domain robustness.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Animal Pose Estimation | Horse-10 | DeepLabCut-EfficientNet-B6 | PCK@0.3 (OOD) | 88.4 | #1 of 8 | Archive leaderboard | report |
| Animal Pose Estimation | Horse-10 | DeepLabCut-EfficientNet-B4 | PCK@0.3 (OOD) | 86.9 | #2 of 8 | Archive leaderboard | report |
| Animal Pose Estimation | Horse-10 | DeepLabCut-RESNET-101 | PCK@0.3 (OOD) | 84.3 | #3 of 8 | Archive leaderboard | report |
| Animal Pose Estimation | Horse-10 | DeepLabCut-RESNET 50 | PCK@0.3 (OOD) | 81.3 | #4 of 8 | Archive leaderboard | report |
| Animal Pose Estimation | Horse-10 | DeepLabCut-MOBILENETV2-1 | PCK@0.3 (OOD) | 77.6 | #5 of 8 | Archive leaderboard | report |
| Animal Pose Estimation | Horse-10 | DeepLabCut-MOBILENETV2 0.35 | PCK@0.3 (OOD) | 63.5 | #6 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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