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Found 23 papers, 18 papers with code
LingoQA: Video Question Answering for Autonomous Driving
To fill this gap, we introduce LingoQA, a benchmark specifically for autonomous driving Video QA.
GAIA-1: A Generative World Model for Autonomous Driving
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging.
Linking vision and motion for self-supervised object-centric perception
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features.
Model-Based Imitation Learning for Urban Driving
Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.
Reimagining an autonomous vehicle
The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development.
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects.
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras.
Ranked #1 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU veh - 224x480 - No vis filter - 100x50 at 0.25 metric)
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects.
Probabilistic Future Prediction for Video Scene Understanding
We present a novel deep learning architecture for probabilistic future prediction from video.
Learning a Spatio-Temporal Embedding for Video Instance Segmentation
We present a novel embedding approach for video instance segmentation.
Urban Driving with Conditional Imitation Learning
As our main contribution, we present an end-to-end conditional imitation learning approach, combining both lateral and longitudinal control on a real vehicle for following urban routes with simple traffic.
Learning to Drive from Simulation without Real World Labels
Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems.
Orthographic Feature Transform for Monocular 3D Object Detection
This allows us to reason holistically about the spatial configuration of the scene in a domain where scale is consistent and distances between objects are meaningful.
3D Object Detection From Monocular Images
Monocular 3D Object Detection
+2
Learning to Drive in a Day
We demonstrate the first application of deep reinforcement learning to autonomous driving.
Concrete Dropout
Dropout is used as a practical tool to obtain uncertainty estimates in large vision models and reinforcement learning (RL) tasks.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
Geometric Loss Functions for Camera Pose Regression with Deep Learning
Deep learning has shown to be effective for robust and real-time monocular image relocalisation.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data.
Ranked #100 on
Semantic Segmentation
on NYU Depth v2
End-to-End Learning of Geometry and Context for Deep Stereo Regression
We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on
Medical Image Segmentation
on RITE
Modelling Uncertainty in Deep Learning for Camera Relocalization
Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset.
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
We present a robust and real-time monocular six degree of freedom relocalization system.
