b'Mingxing Tan'

research

∙ 10/24/2022

LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations

Data augmentations are important in training high-performance 3D object ...

0 Zhaoqi Leng, et al. ∙

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...

0 Pei Sun, et al. ∙

research

∙ 10/10/2022

LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds

Developing neural models that accurately understand objects in 3D point ...

0 Chenxi Liu, et al. ∙

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...

0 Zhaoqi Leng, et al. ∙

research

∙ 03/23/2022

Revisiting Multi-Scale Feature Fusion for Semantic Segmentation

It is commonly believed that high internal resolution combined with expe...

8 Tianjian Meng, et al. ∙

research

∙ 03/08/2022

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

We propose Occupancy Flow Fields, a new representation for motion foreca...

0 Reza Mahjourian, et al. ∙

research

∙ 11/19/2021

Combined Scaling for Zero-shot Transfer Learning

We present a combined scaling method called BASIC that achieves 85.7 zer...

0 Hieu Pham, et al. ∙

research

∙ 06/09/2021

CoAtNet: Marrying Convolution and Attention for All Data Sizes

Transformers have attracted increasing interests in computer vision, but...

0 Zihang Dai, et al. ∙

research

∙ 04/01/2021

EfficientNetV2: Smaller Models and Faster Training

This paper introduces EfficientNetV2, a new family of convolutional netw...

0 Mingxing Tan, et al. ∙

research

∙ 03/23/2021

Robust and Accurate Object Detection via Adversarial Learning

Data augmentation has become a de facto component for training high-perf...

10 Xiangning Chen, et al. ∙

research

∙ 03/21/2021

MoViNets: Mobile Video Networks for Efficient Video Recognition

We present Mobile Video Networks (MoViNets), a family of computation and...

6 Dan Kondratyuk, et al. ∙

research

∙ 02/17/2021

Rethinking Co-design of Neural Architectures and Hardware Accelerators

Neural architectures and hardware accelerators have been two driving for...

15 Yanqi Zhou, et al. ∙

research

∙ 02/10/2021

Searching for Fast Model Families on Datacenter Accelerators

Neural Architecture Search (NAS), together with model scaling, has shown...

0 Sheng Li, et al. ∙

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...

0 Yunyang Xiong, et al. ∙

research

∙ 01/21/2021

PyGlove: Symbolic Programming for Automated Machine Learning

Neural networks are sensitive to hyper-parameter and architecture choice...

30 Daiyi Peng, et al. ∙

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...

10 Arissa Wongpanich, et al. ∙

research

∙ 10/22/2020

Efficient Scale-Permuted Backbone with Learned Resource Distribution

Recently, SpineNet has demonstrated promising results on object detectio...

2 Xianzhi Du, et al. ∙

research

∙ 10/12/2020

Shape-Texture Debiased Neural Network Training

Shape and texture are two prominent and complementary cues for recognizi...

21 Yingwei Li, et al. ∙

research

∙ 06/25/2020

Smooth Adversarial Training

It is commonly believed that networks cannot be both accurate and robust...

13 Cihang Xie, et al. ∙

research

∙ 06/05/2020

AutoHAS: Differentiable Hyper-parameter and Architecture Search

Neural Architecture Search (NAS) has achieved significant progress in pu...

0 Xuanyi Dong, et al. ∙

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...

22 Dan Kondratyuk, et al. ∙

research

∙ 04/30/2020

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

Inverted bottleneck layers, which are built upon depthwise convolutions,...

15 Yunyang Xiong, et al. ∙

research

∙ 03/24/2020

BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models

Neural architecture search (NAS) has shown promising results discovering...

6 Jiahui Yu, et al. ∙

research

∙ 12/10/2019

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

Convolutional neural networks typically encode an input image into a ser...

0 Xianzhi Du, et al. ∙

research

∙ 11/21/2019

Adversarial Examples Improve Image Recognition

Adversarial examples are commonly viewed as a threat to ConvNets. Here w...

24 Cihang Xie, et al. ∙

research

∙ 11/20/2019

Search to Distill: Pearls are Everywhere but not the Eyes

Standard Knowledge Distillation (KD) approaches distill the knowledge of...

0 Yu Liu, et al. ∙

research

∙ 11/20/2019

EfficientDet: Scalable and Efficient Object Detection

Model efficiency has become increasingly important in computer vision. I...

0 Mingxing Tan, et al. ∙

research

∙ 07/22/2019

MixNet: Mixed Depthwise Convolutional Kernels

Depthwise convolution is becoming increasingly popular in modern efficie...

0 Mingxing Tan, et al. ∙

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...

0 Michael S. Ryoo, et al. ∙

research

∙ 05/28/2019

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Convolutional Neural Networks (ConvNets) are commonly developed at a fix...

1 Mingxing Tan, et al. ∙

research

∙ 05/06/2019

Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of c...

53 Andrew Howard, et al. ∙

research

∙ 07/31/2018

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) models for mobile devices ...

0 Mingxing Tan, et al. ∙

Mingxing Tan

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