320 citations · 491 across the 12 of their papers we have counts for
14 papers
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…
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
Anas Mahmoud, Jordan S. K. Hu, Tianshu Kuai +3
An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point repr…
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…
Bayesian Embeddings for Few-Shot Open World Recognition
John Willes, James Harrison, Ali Harakeh +3
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and…
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…
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Matt Angus, Mohamed ElBalkini, Samin Khan +5
In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datas…
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
Alex D. Pon, Oles Andrienko, Ali Harakeh +1
Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or sign…
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
Jason Ku, Ali Harakeh, Steven L. Waslander
With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted c…
Joint 3D Proposal Generation and Object Detection from View Aggregation
Jason Ku, Melissa Mozifian, Jungwook Lee +2
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB images to gen…