Variational Voxel Pseudo Image Tracking

• URL: http://arxiv.org/abs/2302.05914v1
• Date: Sun, 12 Feb 2023 13:34:50 GMT
• Title: Variational Voxel Pseudo Image Tracking
• Authors: Illia Oleksiienko, Paraskevi Nousi, Nikolaos Passalis, Anastasios Tefas and Alexandros Iosifidis
• Abstract summary: Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving. We propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking.
• Score: 127.46919555100543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor. In this paper, we propose a Variational Neural Network-based version of a Voxel Pseudo Image Tracking (VPIT) method for 3D Single Object Tracking. The Variational Feature Generation Network of the proposed Variational VPIT computes features for target and search regions and the corresponding uncertainties, which are later combined using an uncertainty-aware cross-correlation module in one of two ways: by computing similarity between the corresponding uncertainties and adding it to the regular cross-correlation values, or by penalizing the uncertain feature channels to increase influence of the certain features. In experiments, we show that both methods improve tracking performance, while penalization of uncertain features provides the best uncertainty quality.

Related papers

• Uncertainty Propagation through Trained Deep Neural Networks Using Factor Graphs [4.704825771757308]
  Uncertainty propagation seeks to estimate aleatoric uncertainty by propagating input uncertainties to network predictions. Motivated by the complex information flows within deep neural networks, we developed a novel approach by posing uncertainty propagation as a non-linear optimization problem using factor graphs.
  arXiv  Detail & Related papers  (2023-12-10T17:26:27Z)
• Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge [1.7898305876314982]
  Three-dimensional (3D) object detection, a critical robotics operation, has seen significant advancements. Our study integrates the principles of conformal inference with information theoretic measures to perform lightweight, Monte Carlo-free uncertainty estimation. The framework demonstrates comparable or better performance in KITTI 3D object detection benchmarks to similar methods that are not uncertainty-aware.
  arXiv  Detail & Related papers  (2023-09-18T09:02:44Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• Modeling Uncertain Feature Representation for Domain Generalization [49.129544670700525]
  We show that our method consistently improves the network generalization ability on multiple vision tasks. Our methods are simple yet effective and can be readily integrated into networks without additional trainable parameters or loss constraints.
  arXiv  Detail & Related papers  (2023-01-16T14:25:02Z)
• Uncertainty Modeling for Out-of-Distribution Generalization [56.957731893992495]
  We argue that the feature statistics can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features. We improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training.
  arXiv  Detail & Related papers  (2022-02-08T16:09:12Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Accurate and Reliable Forecasting using Stochastic Differential Equations [48.21369419647511]
  It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments. This paper develops SDE-HNN to characterize the interaction between the predictive mean and variance of HNNs for accurate and reliable regression. Experiments on the challenging datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both predictive performance and uncertainty quantification.
  arXiv  Detail & Related papers  (2021-03-28T04:18:11Z)
• Nondeterminism and Instability in Neural Network Optimization [7.6146285961466]
  Nondeterminism in neural network optimization produces uncertainty in performance. We show that all sources of nondeterminism have similar effects on measures of model diversity. We propose two approaches for reducing the effects of instability on run-to-run variability.
  arXiv  Detail & Related papers  (2021-03-08T02:28:18Z)

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.