From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models

2026-07-29

AI Summary

This work addresses the lack of spatial uncertainty modeling in YOLO-Pose for keypoint localization. The authors propose a lightweight, post-hoc probabilistic extension that introduces an additional probability head to predict input-dependent 2×2 covariance matrices, enabling calibrated bivariate Gaussian or Student-t distributions over original keypoints. This is the first approach to equip YOLO-Pose with keypoint-level predictive distributions. A novel evaluation protocol is introduced, combining distribution calibration diagnostics with Average Keypoint Precision (AKP). Experiments on COCO demonstrate that the method effectively supports reliability-based keypoint ranking, with the Student-t formulation yielding more accurate residual distribution fitting. Furthermore, in an aircraft visual landing task, the calibrated covariance enables uncertainty-aware pose estimation and sensor fusion.

Abstract

YOLO-Pose models provide efficient keypoint localization, but do not quantify the associated spatial uncertainty. We introduce a lightweight post-hoc probabilistic extension that augments a trained YOLO-Pose model with calibrated bivariate predictive distributions over keypoint locations, centered at the model's original predictions. Concretely, we train additional probabilistic heads with an importance-weighted negative log-likelihood to predict an input-dependent $2\times2$ dispersion matrix for each keypoint, followed by Gaussian calibration for broad downstream compatibility or Student-$t$ calibration for distributional fidelity. Complementing this, we propose an evaluation protocol that combines a suite of distributional calibration diagnostics with average keypoint precision (AKP), a keypoint-level extension of the COCO AP protocol for assessing reliability rankings. Experiments on COCO show that the learned uncertainty estimates enable effective keypoint-level reliability ranking, Student-$t$ calibration best captures the empirical residual distribution, and uncertainty-based pruning removes unreliable keypoints. A central application-level demonstration is vision-based aircraft landing, where calibrated covariances for runway keypoints support uncertainty-aware aircraft position estimation and downstream sensor fusion.

Problem

• keypoint uncertainty
• spatial uncertainty
• YOLO-Pose
• predictive distributions
• uncertainty quantification

Innovation

• post-hoc uncertainty
• predictive distributions
• YOLO-Pose
• calibrated covariance
• keypoint reliability