Vos: Learning what you don't know by virtual outlier synthesis
Quantification of uncertainty and its applications to complex domain for autonomous vehicles perception system
K Wang, Y Wang, B Liu, J Chen - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Over the last decades, deep neural networks (DNNs) have penetrated all fields of science
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …
Normalizing flow based feature synthesis for outlier-aware object detection
Real-world deployment of reliable object detectors is crucial for applications such as
autonomous driving. However, general-purpose object detectors like Faster R-CNN are …
autonomous driving. However, general-purpose object detectors like Faster R-CNN are …
Can OOD Object Detectors Learn from Foundation Models?
Abstract Out-of-distribution (OOD) object detection is a challenging task due to the absence
of open-set OOD data. Inspired by recent advancements in text-to-image generative models …
of open-set OOD data. Inspired by recent advancements in text-to-image generative models …
Driver drowsiness detection and alert system
R Kannan, P Jahnavi, M Megha - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Driver fatigue is one of the leading causes of accidents worldwide. One of the most reliable
methods of measuring driver fatigue is to detect the driver's drowsiness. Drowsiness and …
methods of measuring driver fatigue is to detect the driver's drowsiness. Drowsiness and …
Conformal prediction for trustworthy detection of railway signals
We present an application of conformal prediction, a form of uncertainty quantification with
guarantees, to the detection of railway signals. State-of-the-art architectures are tested and …
guarantees, to the detection of railway signals. State-of-the-art architectures are tested and …
Identifying out-of-distribution samples in real-time for safety-critical 2D object detection with margin entropy loss
Convolutional Neural Networks (CNNs) are nowadays often employed in vision-based
perception stacks for safetycritical applications such as autonomous driving or Unmanned …
perception stacks for safetycritical applications such as autonomous driving or Unmanned …
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling
Deploying deep learning models in real-world certi ed systems requires the ability to provide
con dence estimates that accurately reflect their uncertainty. In this paper, we demonstrate …
con dence estimates that accurately reflect their uncertainty. In this paper, we demonstrate …