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06/27/2023
Rethinking Closed-loop Training for Autonomous Driving
Recent advances in high-fidelity simulators have enabled closed-loop tra...
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12/17/2021
Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching
Current weakly supervised semantic segmentation (WSSS) frameworks usuall...
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04/08/2021
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
Self-driving vehicles must perceive and predict the future positions of ...
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01/18/2021
Deep Structured Reactive Planning
An intelligent agent operating in the real-world must balance achieving ...
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01/17/2021
End-to-end Interpretable Neural Motion Planner
In this paper, we propose a neural motion planner (NMP) for learning to ...
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01/17/2021
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
Forecasting the future behaviors of dynamic actors is an important task ...
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01/17/2021
Network Automatic Pruning: Start NAP and Take a Nap
Network pruning can significantly reduce the computation and memory foot...
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01/17/2021
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
In the past few years we have seen great advances in 3D object detection...
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01/16/2021
Self-Supervised Representation Learning from Flow Equivariance
Self-supervised representation learning is able to learn semantically me...
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01/07/2021
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
In this paper, we address the important problem in self-driving of forec...
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11/02/2020
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
In this paper, we propose an end-to-end self-driving network featuring a...
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10/29/2020
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Compressing large neural networks is an important step for their deploym...
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08/20/2020
Weakly-supervised 3D Shape Completion in the Wild
3D shape completion for real data is important but challenging, since pa...
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08/17/2020
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communicat...
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08/13/2020
DSDNet: Deep Structured self-Driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDN...
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08/13/2020
End-to-end Contextual Perception and Prediction with Interaction Transformer
In this paper, we tackle the problem of detecting objects in 3D and fore...
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06/16/2020
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We tackle the problem of producing realistic simulations of LiDAR point ...
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05/29/2020
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
We tackle the problem of joint perception and motion forecasting in the ...
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06/12/2018
Differentiable Compositional Kernel Learning for Gaussian Processes
The generalization properties of Gaussian processes depend heavily on th...
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03/24/2018
Learning to Reweight Examples for Robust Deep Learning
Deep neural networks have been shown to be very powerful modeling tools ...
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11/10/2016
