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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2603.15574 |
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| _version_ | 1866908890105380864 |
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| author | Khanal, Aaditya Zhou, Junxiu |
| author_facet | Khanal, Aaditya Zhou, Junxiu |
| contents | The practical deployment gap -- transitioning from controlled multi-view 3D skeleton capture to
unconstrained monocular 2D pose estimation -- introduces a compound domain shift whose safety implications
remain critically underexplored. We present a systematic study of this severe domain shift using a novel
Gym2D dataset (style/viewpoint shift) and the UCF101 dataset (semantic shift). Our Skeleton Transformer
achieves 63.2% cross-subject accuracy on NTU-120 but drops to 1.6% under zero-shot transfer to the Gym
domain and 1.16% on UCF101. Critically, we demonstrate that high Out-Of-Distribution (OOD) detection AUROC
does not guarantee safe selective classification. Standard uncertainty methods fail to detect this
performance drop: the model remains confidently incorrect with 99.6% risk even at 50% coverage across both
OOD datasets. While energy-based scoring (AUROC >= 0.91) and Mahalanobis distance provide reliable
distributional detection signals, such high AUROC scores coexist with poor risk-coverage behavior when
making decisions. A lightweight finetuned gating mechanism restores calibration and enables graceful
abstention, substantially reducing the rate of confident wrong predictions. Our work challenges standard
deployment assumptions, providing a principled safety analysis of both semantic and geometric skeleton
recognition deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15574 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Severe Domain Shift in Skeleton-Based Action Recognition:A Study of Uncertainty Failure in Real-World Gym Environments Khanal, Aaditya Zhou, Junxiu Computer Vision and Pattern Recognition The practical deployment gap -- transitioning from controlled multi-view 3D skeleton capture to unconstrained monocular 2D pose estimation -- introduces a compound domain shift whose safety implications remain critically underexplored. We present a systematic study of this severe domain shift using a novel Gym2D dataset (style/viewpoint shift) and the UCF101 dataset (semantic shift). Our Skeleton Transformer achieves 63.2% cross-subject accuracy on NTU-120 but drops to 1.6% under zero-shot transfer to the Gym domain and 1.16% on UCF101. Critically, we demonstrate that high Out-Of-Distribution (OOD) detection AUROC does not guarantee safe selective classification. Standard uncertainty methods fail to detect this performance drop: the model remains confidently incorrect with 99.6% risk even at 50% coverage across both OOD datasets. While energy-based scoring (AUROC >= 0.91) and Mahalanobis distance provide reliable distributional detection signals, such high AUROC scores coexist with poor risk-coverage behavior when making decisions. A lightweight finetuned gating mechanism restores calibration and enables graceful abstention, substantially reducing the rate of confident wrong predictions. Our work challenges standard deployment assumptions, providing a principled safety analysis of both semantic and geometric skeleton recognition deployment. |
| title | Severe Domain Shift in Skeleton-Based Action Recognition:A Study of Uncertainty Failure in Real-World Gym Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.15574 |