8 citations · 8 across the 2 of their papers we have counts for
8 papers
Estimating Regression Predictive Distributions with Sample Networks
Ali Harakeh, Jordan Hu, Naiqing Guan +2
Estimating the uncertainty in deep neural network predictions is crucial for many real-world applications. A common approach to model uncertainty is to choose a parametric distribu…
Categorical Depth Distribution Network for Monocular 3D Object Detection
Cody Reading, Ali Harakeh, Julia Chae +1
Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main chall…
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
Ali Harakeh, Steven L. Waslander
Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating pred…
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Di Feng, Ali Harakeh, Steven Waslander +1
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and ma…
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
Ali Harakeh, Michael Smart, Steven L. Waslander
When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertaint…
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
Jungwook Lee, Sean Walsh, Ali Harakeh +1
Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations. Reducing both task complexity and the amo…