Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration

• URL: http://arxiv.org/abs/2306.08981v1
• Date: Thu, 15 Jun 2023 09:20:07 GMT
• Title: Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration
• Authors: Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth, Azarm Nowzad and Sahin Albayrak
• Abstract summary: Existing works estimate the aleatoric uncertainty of object detectors by modeling their localization output as a Gaussian distribution. We identify three aspects that are unaddressed in the state of the art, but warrant further exploration. We overcome these limitations by: (1) implementing loss attenuation in EfficientDet, and proposing two deterministic methods for the exact and fast propagation of the output distribution, and (2) demonstrating on the KITTI and BDD100K datasets that the predicted uncertainty is miscalibrated.
• Score: 4.199844472131921
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robustly and accurately localizing objects in real-world environments can be challenging due to noisy data, hardware limitations, and the inherent randomness of physical systems. To account for these factors, existing works estimate the aleatoric uncertainty of object detectors by modeling their localization output as a Gaussian distribution $\mathcal{N}(\mu,\,\sigma^{2})\,$, and training with loss attenuation. We identify three aspects that are unaddressed in the state of the art, but warrant further exploration: (1) the efficient and mathematically sound propagation of $\mathcal{N}(\mu,\,\sigma^{2})\,$ through non-linear post-processing, (2) the calibration of the predicted uncertainty, and (3) its interpretation. We overcome these limitations by: (1) implementing loss attenuation in EfficientDet, and proposing two deterministic methods for the exact and fast propagation of the output distribution, (2) demonstrating on the KITTI and BDD100K datasets that the predicted uncertainty is miscalibrated, and adapting two calibration methods to the localization task, and (3) investigating the correlation between aleatoric uncertainty and task-relevant error sources. Our contributions are: (1) up to five times faster propagation while increasing localization performance by up to 1\%, (2) up to fifteen times smaller expected calibration error, and (3) the predicted uncertainty is found to correlate with occlusion, object distance, detection accuracy, and image quality.

Related papers

• Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction [95.75771195913046]
  We propose a risk-controlling quantile neural operator, a distribution-free, finite-sample functional calibration conformal prediction method. We provide a theoretical calibration guarantee on the coverage rate, defined as the expected percentage of points on the function domain. Empirical results on a 2D Darcy flow and a 3D car surface pressure prediction task validate our theoretical results.
  arXiv  Detail & Related papers  (2024-02-02T23:43:28Z)
• Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework [8.572441599469597]
  We study high-confidence off-policy evaluation in the context of infinite-horizon Markov decision processes. The objective is to establish a confidence interval (CI) for the target policy value using only offline data pre-collected from unknown behavior policies. We show that our algorithm is sample-efficient, error-robust, and provably convergent even in non-linear function approximation settings.
  arXiv  Detail & Related papers  (2023-09-23T06:35:44Z)
• Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization [81.29406957201458]
  Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection. We propose to model the rotated objects as Gaussian distributions. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
  arXiv  Detail & Related papers  (2022-09-22T07:50:48Z)
• DBCal: Density Based Calibration of classifier predictions for uncertainty quantification [0.0]
  We present a technique that quantifies the uncertainty of predictions from a machine learning method. We prove that our method provides an accurate estimate of the probability that the outputs of two neural networks are correct.
  arXiv  Detail & Related papers  (2022-04-01T01:03:41Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation [25.003116148843525]
  Surface normal estimation from a single image is an important task in 3D scene understanding. In this paper, we address two limitations shared by the existing methods: the inability to estimate the aleatoric uncertainty and lack of detail in the prediction. We present a novel decoder framework where pixel-wise perceptrons are trained on a subset of pixels sampled based on the estimated uncertainty.
  arXiv  Detail & Related papers  (2021-09-20T23:30:04Z)
• Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
  Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
  arXiv  Detail & Related papers  (2021-07-08T15:19:36Z)
• Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs [29.84563789289183]
  Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via discrete state transitions over recurrent timesteps. The uncertainty of the model can be quantified by running a prediction several times, each time sampling from the recurrent state transition distribution.
  arXiv  Detail & Related papers  (2020-11-24T10:35:28Z)
• Localization Uncertainty Estimation for Anchor-Free Object Detection [48.931731695431374]
  There are several limitations of the existing uncertainty estimation methods for anchor-based object detection. We propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets that are homogeneous, so that it can tell which direction is uncertain.
  arXiv  Detail & Related papers  (2020-06-28T13:49:30Z)
• Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality [92.35257908210316]
  We study the problem of estimating a low-rank tensor given incomplete and corrupted observations. We find that it attains unimprovable rates $ell-2$ accuracy.
  arXiv  Detail & Related papers  (2020-06-15T17:47:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.