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Found 12 papers, 5 papers with code
SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling
We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary objects.
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
We evaluate the effectiveness of XC scores via the task of distinguishing true positive (TP) and false positive (FP) detected objects in the KITTI and Waymo datasets.
3D Object Detection
Explainable Artificial Intelligence (XAI)
+1
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Detecting OOD inputs is challenging and essential for the safe deployment of models.
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance.
The missing link: Developing a safety case for perception components in automated driving
In this paper, we propose the Integration Safety Case for Perception (ISCaP), a generic template for such a linking safety argument specifically tailored for perception components.
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches
In addition to comparing several OODD approaches using our proposed robustness score, we demonstrate that some optimization methods provide better solutions for OODD approaches.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Several approaches have been proposed to detect OOD inputs, but the detection task is still an ongoing challenge.
Out-of-distribution Detection in Classifiers via Generation
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving Confident-Classifiers For Out-of-distribution Detection
In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called “confident-classifier” by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KLdivergence between the predictive distribution of OOD samples in the low-density“boundary” of in-distribution and the uniform distribution (maximizing the entropy of the outputs).
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Analysis of Confident-Classifiers for Out-of-distribution Detection
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems.
Calibrating Uncertainties in Object Localization Task
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making.
