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ARXIV:2603.15574 · ACTION RECOGNITION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.15574ACTION RECOGNITIONSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism.
Opportunity summary
Pain A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism. We present a systematic study of this severe domain shift using a novel Gym2D dataset (style/viewpoint shift)…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Action Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism.
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Paper Pack
10.48550/arXiv.2603.15574A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism.
Abstract
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.
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Proof status
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Dimensions overall score 4.0
PROBLEM
A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism. We present a systematic study of this severe domain shift using a novel Gym2D dataset (style/viewpoint shift) and the UCF101 dataset (semantic shift).
METHOD
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 s...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
WHY NOW
Action Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism. We present a systematic study of this severe domain shift using a novel Gym2D dataset (style/viewpoint shift) and the UCF101 dataset (semantic shift).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Action Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A study on improving safety in skeleton-based action recognition through uncertainty analysis and a novel gating mechanism.
Segment
Action Recognition
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4.0/10 public viability
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reason
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partial
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DEFENSIBILITY
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