Search Results for author: Ming Lin

Found 23 papers, 4 papers with code

Fine-Grained AutoAugmentation for Multi-Label Classification

no code implementations12 Jul 2021 Ya Wang, Hesen Chen, Fangyi Zhang, Yaohua Wang, Xiuyu Sun, Ming Lin, Hao Li

Data augmentation is a commonly used approach to improving the generalization of deep learning models.

Classification Data Augmentation +3

KVT: k-NN Attention for Boosting Vision Transformers

no code implementations28 May 2021 Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, Rong Jin

A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies.

Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving

no code implementations15 Mar 2021 Shivam Akhauri, Laura Zheng, Tom Goldstein, Ming Lin

Finally, based on the results of our ablation study, we propose a transfer learning pipeline that uses saliency maps and physical features extracted from a source model to enhance the performance of a target model.

Autonomous Driving Decision Making +1

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition

1 code implementation1 Feb 2021 Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.

Neural Architecture Search

Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples

no code implementations1 Jan 2021 Yu Shen, Laura Yu Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin

To ensure the wide adoption and safety of autonomous driving, the vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +2

Learning Accurate Entropy Model with Global Reference for Image Compression

no code implementations ICLR 2021 Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Hao Li, Rong Jin

In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate.

Image Compression

Robust Finite Mixture Regression for Heterogeneous Targets

no code implementations12 Oct 2020 Jian Liang, Kun Chen, Ming Lin, ChangShui Zhang, Fei Wang

FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the observed samples given the features.

Feature Selection

WeMix: How to Better Utilize Data Augmentation

no code implementations3 Oct 2020 Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin

To this end, we develop two novel algorithms, termed "AugDrop" and "MixLoss", to correct the data bias in the data augmentation.

Data Augmentation

Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation

no code implementations23 Jul 2020 Shivam Akhauri, Laura Zheng, Ming Lin

Simulation data can be utilized to extend real-world driving data in order to cover edge cases, such as vehicle accidents.

Autonomous Driving Transfer Learning

Neural Architecture Design for GPU-Efficient Networks

2 code implementations24 Jun 2020 Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies.

Neural Architecture Search

Knapsack Pruning with Inner Distillation

1 code implementation19 Feb 2020 Yonathan Aflalo, Asaf Noy, Ming Lin, Itamar Friedman, Lihi Zelnik

Through this we produce compact architectures with the same FLOPs as EfficientNet-B0 and MobileNetV3 but with higher accuracy, by $1\%$ and $0. 3\%$ respectively on ImageNet, and faster runtime on GPU.

Knowledge Distillation Network Pruning +1

Differentiable Cloth Simulation for Inverse Problems

1 code implementation NeurIPS 2019 Junbang Liang, Ming Lin, Vladlen Koltun

We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks.

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

no code implementations3 Jun 2019 Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin

We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.

Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

no code implementations19 Jul 2017 Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li

However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.

Computed Tomography (CT)

Nonconvex One-bit Single-label Multi-label Learning

no code implementations17 Mar 2017 Shuang Qiu, Tingjin Luo, Jieping Ye, Ming Lin

We study an extreme scenario in multi-label learning where each training instance is endowed with a single one-bit label out of multiple labels.

Multi-Label Learning

The Second Order Linear Model

no code implementations2 Mar 2017 Ming Lin, Shuang Qiu, Bin Hong, Jieping Ye

We show that the conventional gradient descent heuristic is biased by the skewness of the distribution therefore is no longer the best practice of learning the SLM.

A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing

no code implementations NeurIPS 2016 Ming Lin, Jieping Ye

We develop an efficient alternating framework for learning a generalized version of Factorization Machine (gFM) on steaming data with provable guarantees.

Matrix Completion

Strategies for Searching Video Content with Text Queries or Video Examples

no code implementations17 Jun 2016 Shoou-I Yu, Yi Yang, Zhongwen Xu, Shicheng Xu, Deyu Meng, Zexi Mao, Zhigang Ma, Ming Lin, Xuanchong Li, Huan Li, Zhenzhong Lan, Lu Jiang, Alexander G. Hauptmann, Chuang Gan, Xingzhong Du, Xiaojun Chang

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.

Event Detection Video Retrieval

Handcrafted Local Features are Convolutional Neural Networks

no code implementations16 Nov 2015 Zhenzhong Lan, Shoou-I Yu, Ming Lin, Bhiksha Raj, Alexander G. Hauptmann

We approach this problem by first showing that local handcrafted features and Convolutional Neural Networks (CNNs) share the same convolution-pooling network structure.

Action Recognition Optical Flow Estimation

The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition

no code implementations17 May 2015 Zhenzhong Lan, Dezhong Yao, Ming Lin, Shoou-I Yu, Alexander Hauptmann

First, we propose a two-stream Stacked Convolutional Independent Subspace Analysis (ConvISA) architecture to show that unsupervised learning methods can significantly boost the performance of traditional local features extracted from data-independent models.

Action Recognition Multi-class Classification +2

Long-short Term Motion Feature for Action Classification and Retrieval

no code implementations13 Feb 2015 Zhenzhong Lan, Xuanchong Li, Ming Lin, Alexander G. Hauptmann

Therefore, they need to occur frequently enough in the videos and to be be able to tell the difference among different types of motions.

Action Classification Classification +2

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