research
∙
10/24/2022
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations
Data augmentations are important in training high-performance 3D object ...
share
research
∙
10/13/2022
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
3D object detection in point clouds is a core component for modern robot...
research
∙
10/10/2022
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds
Developing neural models that accurately understand objects in 3D point ...
research
∙
04/26/2022
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
Cross-entropy loss and focal loss are the most common choices when train...
research
∙
03/23/2022
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation
It is commonly believed that high internal resolution combined with expe...
research
∙
03/08/2022
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
We propose Occupancy Flow Fields, a new representation for motion foreca...
research
∙
11/19/2021
Combined Scaling for Zero-shot Transfer Learning
We present a combined scaling method called BASIC that achieves 85.7 zer...
research
∙
06/09/2021
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Transformers have attracted increasing interests in computer vision, but...
research
∙
04/01/2021
EfficientNetV2: Smaller Models and Faster Training
This paper introduces EfficientNetV2, a new family of convolutional netw...
research
∙
03/23/2021
Robust and Accurate Object Detection via Adversarial Learning
Data augmentation has become a de facto component for training high-perf...
research
∙
03/21/2021
MoViNets: Mobile Video Networks for Efficient Video Recognition
We present Mobile Video Networks (MoViNets), a family of computation and...
research
∙
02/17/2021
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Neural architectures and hardware accelerators have been two driving for...
research
∙
02/10/2021
Searching for Fast Model Families on Datacenter Accelerators
Neural Architecture Search (NAS), together with model scaling, has shown...
research
∙
02/07/2021
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
Transformers have emerged as a powerful tool for a broad range of natura...
research
∙
01/21/2021
PyGlove: Symbolic Programming for Automated Machine Learning
Neural networks are sensitive to hyper-parameter and architecture choice...
research
∙
10/30/2020
Training EfficientNets at Supercomputer Scale: 83 Accuracy in One Hour
EfficientNets are a family of state-of-the-art image classification mode...
research
∙
10/22/2020
Efficient Scale-Permuted Backbone with Learned Resource Distribution
Recently, SpineNet has demonstrated promising results on object detectio...
research
∙
10/12/2020
Shape-Texture Debiased Neural Network Training
Shape and texture are two prominent and complementary cues for recognizi...
research
∙
06/25/2020
Smooth Adversarial Training
It is commonly believed that networks cannot be both accurate and robust...
research
∙
06/05/2020
AutoHAS: Differentiable Hyper-parameter and Architecture Search
Neural Architecture Search (NAS) has achieved significant progress in pu...
research
∙
05/01/2020
When Ensembling Smaller Models is More Efficient than Single Large Models
Ensembling is a simple and popular technique for boosting evaluation per...
research
∙
04/30/2020
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Inverted bottleneck layers, which are built upon depthwise convolutions,...
research
∙
03/24/2020
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Neural architecture search (NAS) has shown promising results discovering...
research
∙
12/10/2019
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
Convolutional neural networks typically encode an input image into a ser...
research
∙
11/21/2019
Adversarial Examples Improve Image Recognition
Adversarial examples are commonly viewed as a threat to ConvNets. Here w...
research
∙
11/20/2019
Search to Distill: Pearls are Everywhere but not the Eyes
Standard Knowledge Distillation (KD) approaches distill the knowledge of...
research
∙
11/20/2019
EfficientDet: Scalable and Efficient Object Detection
Model efficiency has become increasingly important in computer vision. I...
research
∙
07/22/2019
MixNet: Mixed Depthwise Convolutional Kernels
Depthwise convolution is becoming increasingly popular in modern efficie...
research
∙
05/30/2019
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Learning to represent videos is a very challenging task both algorithmic...
research
∙
05/28/2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Convolutional Neural Networks (ConvNets) are commonly developed at a fix...
research
∙
05/06/2019
Searching for MobileNetV3
We present the next generation of MobileNets based on a combination of c...
research
∙
07/31/2018
