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01/12/2023
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
An effective framework for learning 3D representations for perception ta...
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11/24/2022
Estimating Regression Predictive Distributions with Sample Networks
Estimating the uncertainty in deep neural network predictions is crucial...
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07/29/2021
Bayesian Embeddings for Few-Shot Open World Recognition
As autonomous decision-making agents move from narrow operating environm...
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03/01/2021
Categorical Depth Distribution Network for Monocular 3D Object Detection
Monocular 3D object detection is a key problem for autonomous vehicles, ...
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01/13/2021
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
Predictive uncertainty estimation is an essential next step for the reli...
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11/20/2020
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Capturing uncertainty in object detection is indispensable for safe auto...
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03/09/2019
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
One of the challenging aspects of incorporating deep neural networks int...
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07/16/2018
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
Training 3D object detectors for autonomous driving has been limited to ...
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07/16/2018
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
In training deep neural networks for semantic segmentation, the main lim...
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06/20/2018
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
Traffic light and sign detectors on autonomous cars are integral for roa...
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01/31/2018
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
With the rise of data driven deep neural networks as a realization of un...
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12/06/2017