Search Results for author: Mingxing Tan

Found 36 papers, 19 papers with code

Revisiting Multi-Scale Feature Fusion for Semantic Segmentation

no code implementations23 Mar 2022 Tianjian Meng, Golnaz Ghiasi, Reza Mahjorian, Quoc V. Le, Mingxing Tan

It is commonly believed that high internal resolution combined with expensive operations (e. g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in slow speed and large memory usage.

Semantic Segmentation

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection

1 code implementation15 Mar 2022 Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan Yuille, Mingxing Tan

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.

3D Object Detection Autonomous Driving +1

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

no code implementations8 Mar 2022 Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Ben Sapp, Dragomir Anguelov

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving.

Motion Estimation Motion Forecasting

Combined Scaling for Open-Vocabulary Image Classification

no code implementations19 Nov 2021 Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le

Second, while increasing the dataset size and the model size has been the defacto method to improve the performance of deep learning models like BASIC, the effect of a large contrastive batch size on such contrastive-trained image-text models is not well-understood.

Ranked #2 on Zero-Shot Transfer Image Classification on ImageNet (using extra training data)

Classification Contrastive Learning +3

CoAtNet: Marrying Convolution and Attention for All Data Sizes

8 code implementations NeurIPS 2021 Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan

Transformers have attracted increasing interests in computer vision, but they still fall behind state-of-the-art convolutional networks.

Ranked #3 on Image Classification on ImageNet (using extra training data)

Image Classification

EfficientNetV2: Smaller Models and Faster Training

14 code implementations1 Apr 2021 Mingxing Tan, Quoc V. Le

By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87. 3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2. 0% accuracy while training 5x-11x faster using the same computing resources.

Data Augmentation Image Classification +1

Robust and Accurate Object Detection via Adversarial Learning

1 code implementation CVPR 2021 Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong

Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection.

AutoML Data Augmentation +1

MoViNets: Mobile Video Networks for Efficient Video Recognition

2 code implementations CVPR 2021 Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong

We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.

 Ranked #1 on Action Classification on Kinetics-600 (GFLOPs metric)

Action Classification Action Recognition +2

Searching for Fast Model Families on Datacenter Accelerators

no code implementations CVPR 2021 Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, Norman P. Jouppi

On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency.

Neural Architecture Search

Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour

no code implementations30 Oct 2020 Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc Le, Yang You, Sameer Kumar

EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks.

Image Classification

Shape-Texture Debiased Neural Network Training

1 code implementation ICLR 2021 Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie

To prevent models from exclusively attending on a single cue in representation learning, we augment training data with images with conflicting shape and texture information (eg, an image of chimpanzee shape but with lemon texture) and, most importantly, provide the corresponding supervisions from shape and texture simultaneously.

Adversarial Robustness Data Augmentation +2

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

no code implementations ICML 2020 Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans

This is achieved by layerwise imitation, that is, forcing the thin network to mimic the intermediate outputs of the wide network from layer to layer.

Model Compression

Smooth Adversarial Training

1 code implementation25 Jun 2020 Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, Quoc V. Le

SAT also works well with larger networks: it helps EfficientNet-L1 to achieve 82. 2% accuracy and 58. 6% robustness on ImageNet, outperforming the previous state-of-the-art defense by 9. 5% for accuracy and 11. 6% for robustness.

Adversarial Defense Adversarial Robustness

AutoHAS: Efficient Hyperparameter and Architecture Search

no code implementations5 Jun 2020 Xuanyi Dong, Mingxing Tan, Adams Wei Yu, Daiyi Peng, Bogdan Gabrys, Quoc V. Le

Efficient hyperparameter or architecture search methods have shown remarkable results, but each of them is only applicable to searching for either hyperparameters (HPs) or architectures.

Hyperparameter Optimization Neural Architecture Search +1

When Ensembling Smaller Models is More Efficient than Single Large Models

no code implementations1 May 2020 Dan Kondratyuk, Mingxing Tan, Matthew Brown, Boqing Gong

Ensembling is a simple and popular technique for boosting evaluation performance by training multiple models (e. g., with different initializations) and aggregating their predictions.

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

4 code implementations CVPR 2021 Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen

By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.

Neural Architecture Search Object Detection

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

1 code implementation ECCV 2020 Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, Quoc Le

Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs.

Neural Architecture Search

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

5 code implementations CVPR 2020 Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song

We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.

General Classification Image Classification +4

Adversarial Examples Improve Image Recognition

6 code implementations CVPR 2020 Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan Yuille, Quoc V. Le

We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger.

Image Classification

Search to Distill: Pearls are Everywhere but not the Eyes

no code implementations CVPR 2020 Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang

Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture.

Ensemble Learning Face Recognition +3

Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search

no code implementations25 Sep 2019 Krzysztof Maziarz, Mingxing Tan, Andrey Khorlin, Kuang-Yu Samuel Chang, Andrea Gesmundo

We show that the Evo-NAS agent outperforms both neural and evolutionary agents when applied to architecture search for a suite of text and image classification benchmarks.

Image Classification Neural Architecture Search +1

Scaling Up Neural Architecture Search with Big Single-Stage Models

no code implementations25 Sep 2019 Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Quoc Le

In this work, we propose BigNAS, an approach that simplifies this workflow and scales up neural architecture search to target a wide range of model sizes simultaneously.

Neural Architecture Search

MixConv: Mixed Depthwise Convolutional Kernels

12 code implementations22 Jul 2019 Mingxing Tan, Quoc V. Le

In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency.

AutoML Image Classification +1

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

107 code implementations ICML 2019 Mingxing Tan, Quoc V. Le

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.

Ranked #2 on Fine-Grained Image Classification on Birdsnap (using extra training data)

Fine-Grained Image Classification Neural Architecture Search +1

Evolutionary-Neural Hybrid Agents for Architecture Search

no code implementations24 Nov 2018 Krzysztof Maziarz, Mingxing Tan, Andrey Khorlin, Marin Georgiev, Andrea Gesmundo

We show that the Evo-NAS agent outperforms both neural and evolutionary agents when applied to architecture search for a suite of text and image classification benchmarks.

General Classification Image Classification +3

MnasNet: Platform-Aware Neural Architecture Search for Mobile

16 code implementations CVPR 2019 Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.

Image Classification Neural Architecture Search +1

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