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Self-Supervised Monocular Depth Estimation by Digging into Uncertainty Quantification
Based on well-designed network architectures and objective functions, self-supervised monocular depth estimation has made great progress. However,...
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Self-paced uncertainty estimation for one-shot person re-identification
The one-shot person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from
X toY . The first is model...
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AMTLUS: Attention-guided multi-task learning with uncertainty estimation in skin lesion segmentation and classification
Skin lesion segmentation and classification from dermoscopic images have emerged as pivotal research topics, playing vibrant role in early detection...
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A review of predictive uncertainty estimation with machine learning
Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of...
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Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation
Aleatoric uncertainty estimation is a critical step in medical image segmentation. Most techniques for estimating aleatoric uncertainty for...
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Uncertainty estimation based adversarial attack in multi-class classification
Model uncertainty has gained popularity in machine learning due to the overconfident predictions derived from standard neural networks which are not...
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Localization Uncertainty Estimation for Anchor-Free Object Detection
Since many safety-critical systems, such as surgical robots and autonomous driving cars operate in unstable environments with sensor noise and...
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GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can...
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Data-driven and uncertainty-aware robust airstrip surface estimation
The performances of aircraft braking control systems are strongly influenced by the tire friction force experienced during the braking phase. The...
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Cardiac arrhythmia classification with rejection of ECG recordings based on uncertainty estimation from deep neural networks
With the development of deep learning-based methods, automated classification of electrocardiograms (ECGs) has recently gained much attention....
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Causal Interpretability and Uncertainty Estimation in Mixture Density Networks
Neural network implementations have predominantly been a black box lacking both in interpretability and estimation of uncertainty. In this study, we...
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Towards Improved Intermediate Layer Variational Inference for Uncertainty Estimation
DNNs have been excelling on many computer vision tasks, achieving many milestones and are continuing to prove their validity. It is important for...
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Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout
Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown...
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Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction
MRI reconstruction techniques based on deep learning have led to unprecedented reconstruction quality especially in highly accelerated settings....
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Variational Depth Networks: Uncertainty-Aware Monocular Self-supervised Depth Estimation
Using self-supervised learning, neural networks are trained to predict depth from a single image without requiring ground-truth annotations. However,...
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Uncertainty-Aware Face Recognition
In this chapter, we introduced the motivation of data uncertainty estimation in deep face recognition systems as well as its applications. From a...
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Uncertainty Estimation in Liver Tumor Segmentation Using the Posterior Bootstrap
Deep learning-based medical image segmentation is widely used and has achieved the state-of-the-art segmentation performance, in which nnU-Net is a...
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Gradient-Based Uncertainty for Monocular Depth Estimation
In monocular depth estimation, disturbances in the image context, like moving objects or reflecting materials, can easily lead to erroneous...
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Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
In this work, we introduce Deep Bingham Networks (DBN) , a generic framework that can naturally handle pose-related uncertainties and ambiguities...
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Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts
In the field of medical imaging, computed tomography (CT) scans have become crucial for the detection and management of anatomical abnormalities....