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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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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
Cost-Efficient Online Hyperparameter Optimization
Recent work on hyperparameters optimization (HPO) has shown the possibil...
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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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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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08/04/2020
LoCo: Local Contrastive Representation Learning
Deep neural nets typically perform end-to-end backpropagation to learn t...
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07/30/2020
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Obtaining precise instance segmentation masks is of high importance in m...
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12/05/2019
PolyTransform: Deep Polygon Transformer for Instance Segmentation
In this paper, we propose PolyTransform, a novel instance segmentation a...
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10/17/2019
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Self-driving vehicles plan around both static and dynamic objects, apply...
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10/10/2019
Learning to Remember from a Multi-Task Teacher
Recent studies on catastrophic forgetting during sequential learning typ...
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09/27/2019
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In this paper, we propose the differentiable mask-matching network (DMM-...
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07/30/2019
Deformable Filter Convolution for Point Cloud Reasoning
Point clouds are the native output of many real-world 3D sensors. To bor...
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04/18/2019
Deep Rigid Instance Scene Flow
In this paper we tackle the problem of scene flow estimation in the cont...
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01/12/2019
UPSNet: A Unified Panoptic Segmentation Network
In this paper, we propose a unified panoptic segmentation network (UPSNe...
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03/21/2018
Inference in Probabilistic Graphical Models by Graph Neural Networks
A useful computation when acting in a complex environment is to infer th...
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03/16/2018
Reviving and Improving Recurrent Back-Propagation
In this paper, we revisit the recurrent back-propagation (RBP) algorithm...
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03/17/2017
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model geo...
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11/23/2016
