8 citations · 8 across the 4 of their papers we have counts for
8 papers · 1 filter
Image-to-Lidar Relational Distillation for Autonomous Driving Data
Anas Mahmoud, Ali Harakeh, Steven Waslander
Pre-trained on extensive and diverse multi-modal datasets, 2D foundation models excel at addressing 2D tasks with little or no downstream supervision, owing to their robust represe…
BACS: Background Aware Continual Semantic Segmentation
Mostafa ElAraby, Ali Harakeh, Liam Paull
Semantic segmentation plays a crucial role in enabling comprehensive scene understanding for robotic systems. However, generating annotations is challenging, requiring labels for e…
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…