Search Results for author:
Found 22 papers, 6 papers with code
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection
In this paper, we propose two novel techniques: InverseAug that inverses geometric-related augmentations, e. g., rotation, to enable accurate geometric alignment between lidar points and image pixels, and LearnableAlign that leverages cross-attention to dynamically capture the correlations between image and lidar features during fusion.
Scene Transformer: A unified architecture for predicting future trajectories of multiple agents
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
3D object detection is vital for many robotics applications.
Scene Transformer: A unified architecture for predicting multiple agent trajectories
In this work, we formulate a model for predicting the behavior of all agents jointly, producing consistent futures that account for interactions between agents.
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
3D-MAN: 3D Multi-frame Attention Network for Object Detection
In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset.
Pseudo-labeling for Scalable 3D Object Detection
To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset
Furthermore, we introduce a new set of metrics that provides a comprehensive evaluation of both single agent and joint agent interaction motion forecasting models.
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.
Streaming Object Detection for 3-D Point Clouds
This built-in data capture latency is artificial, and based on treating the point cloud as a camera image in order to leverage camera-inspired architectures.
Improving 3D Object Detection through Progressive Population Based Augmentation
Data augmentation has been widely adopted for object detection in 3D point clouds.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both.
StarNet: Targeted Computation for Object Detection in Point Clouds
We show how our redesign---namely using only local information and using sampling instead of learned proposals---leads to a significantly more flexible and adaptable system: we demonstrate how we can vary the computational cost of a single trained StarNet without retraining, and how we can target proposals towards areas of interest with priors and heuristics.
Learning a Multi-Domain Curriculum for Neural Machine Translation
Most data selection research in machine translation focuses on improving a single domain.
Using Videos to Evaluate Image Model Robustness
Compared to previous studies on adversarial examples and synthetic distortions, natural robustness captures a more diverse set of common image transformations that occur in the natural environment.
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks.
Ranked #680 on
Image Classification
on ImageNet
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.
Ranked #4 on
Fine-Grained Image Classification
on Birdsnap
(using extra training data)
Domain Adaptive Transfer Learning with Specialist Models
Our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning.
Ranked #2 on
Fine-Grained Image Classification
on Stanford Cars
(using extra training data)
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
We show that the soft reconstruction cost can also be used to prevent replicated features in tiled convolutional neural networks.
Ranked #116 on
Image Classification
on STL-10
Sparse Filtering
Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification.
Tiled convolutional neural networks
Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture.
