Search Results for author: Min Wang

Found 32 papers, 10 papers with code

Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion

no code implementations EMNLP 2021 Xiaobao Guo, Adams Kong, Huan Zhou, Xianfeng Wang, Min Wang

Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.

Representation Learning

HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis

2 code implementations9 May 2022 Wei Dai, Rui Liu, Tianyi Wu, Min Wang, Jianqin Yin, Jun Liu

The new light HierAttn network has the potential in promoting the use of deep learning in clinics and allowing patients for early diagnosis of skin disorders with personal devices.

Rethinking Efficient Lane Detection via Curve Modeling

1 code implementation4 Mar 2022 Zhengyang Feng, Shaohua Guo, Xin Tan, Ke Xu, Min Wang, Lizhuang Ma

This paper presents a novel parametric curve-based method for lane detection in RGB images.

Lane Detection

Diverse facial inpainting guided by exemplars

no code implementations13 Feb 2022 Wanglong Lu, Hanli Zhao, Xianta Jiang, Xiaogang Jin, Min Wang, Jiankai Lyu, Kaijie Shi

Facial image inpainting is a task of filling visually realistic and semantically meaningful contents for missing or masked pixels in a face image.

Facial Inpainting

Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness

no code implementations1 Jan 2022 Hao Yang, Min Wang, Zhengfei Yu, Yun Zhou

Extensive experiments on well-known white- and black-box attacks show that MFDV-SNN achieves a significant improvement over existing methods, which indicates that it is a simple but effective method to improve model robustness.

Adversarial Robustness

Learning Token-based Representation for Image Retrieval

1 code implementation12 Dec 2021 Hui Wu, Min Wang, Wengang Zhou, Yang Hu, Houqiang Li

Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention.

Image Retrieval

Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks

no code implementations2 Nov 2021 Xiang Huang, Hongsheng Liu, Beiji Shi, Zidong Wang, Kang Yang, Yang Li, Bingya Weng, Min Wang, Haotian Chu, Jing Zhou, Fan Yu, Bei Hua, Lei Chen, Bin Dong

In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.

Contextual Similarity Aggregation with Self-attention for Visual Re-ranking

1 code implementation NeurIPS 2021 Jianbo Ouyang, Hui Wu, Min Wang, Wengang Zhou, Houqiang Li

Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms.

Content-Based Image Retrieval Data Augmentation +1

DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control

no code implementations12 Sep 2021 Libing Wu, Min Wang, Dan Wu, Jia Wu

Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.

Graph Attention

The 2nd Anti-UAV Workshop & Challenge: Methods and Results

no code implementations23 Aug 2021 Jian Zhao, Gang Wang, Jianan Li, Lei Jin, Nana Fan, Min Wang, Xiaojuan Wang, Ting Yong, Yafeng Deng, Yandong Guo, Shiming Ge, Guodong Guo

The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.

Object Tracking

SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature

1 code implementation CVPR 2021 Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang

For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.

Dimensionality Reduction

The Probability Evolution of Corporate Control Power

no code implementations3 Jun 2021 Jie He, Min Wang

How does the control power of corporate top1 shareholder arise?

SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature

1 code implementation Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang

For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.

Dimensionality Reduction

THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation

1 code implementation AAAI Technical Track on Machine Learning 2021 Mengyun Chen, Kaixin Gao, Xiaolei Liu, Zidong Wang, Ningxi Ni, Qian Zhang, Lei Chen, Chao Ding, ZhengHai Huang, Min Wang, Shuangling Wang, Fan Yu, Xinyuan Zhao, Dachuan Xu

It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.

IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT

no code implementations28 Mar 2021 Min Wang, Shanchen Pang, Tong Ding, Sibo Qiao, Xue Zhai, Shuo Wang, Neal N. Xiong, Zhengwen Huang

In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).

Few-Shot Learning

A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations

no code implementations5 Jan 2021 Jianfeng Lu, Yulong Lu, Min Wang

This paper concerns the a priori generalization analysis of the Deep Ritz Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for solving high dimensional partial differential equations.

Learning Deep Local Features With Multiple Dynamic Attentions for Large-Scale Image Retrieval

1 code implementation ICCV 2021 Hui Wu, Min Wang, Wengang Zhou, Houqiang Li

To this end, we propose a novel deep local feature learning architecture to simultaneously focus on multiple discriminative local patterns in an image.

Image Retrieval Metric Learning

AsymptoticNG: A regularized natural gradient optimization algorithm with look-ahead strategy

no code implementations24 Dec 2020 Zedong Tang, Fenlong Jiang, Junke Song, Maoguo Gong, Hao Li, Fan Yu, Zidong Wang, Min Wang

Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD).

Eigenvalue-corrected Natural Gradient Based on a New Approximation

no code implementations27 Nov 2020 Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang, Min Wang, Shuangling Wang, Zidong Wang, Dachuan Xu, Fan Yu

Using second-order optimization methods for training deep neural networks (DNNs) has attracted many researchers.

A Trace-restricted Kronecker-Factored Approximation to Natural Gradient

no code implementations21 Nov 2020 Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang, Min Wang, Zidong Wang, Dachuan Xu, Fan Yu

There have been many attempts to use second-order optimization methods for training deep neural networks.

Empirical distributions of the robustified $t$-test statistics

no code implementations6 Jul 2018 Chanseok Park, Min Wang

Based on the median and the median absolute deviation estimators, and the Hodges-Lehmann and Shamos estimators, robustified analogues of the conventional $t$-test statistic are proposed.


Weight-importance sparse training in keyword spotting

no code implementations2 Jul 2018 Sihao Xue, Zhenyi Ying, Fan Mo, Min Wang, Jue Sun

Besides this, at most of time, ASR system is used to deal with real-time problem such as keyword spotting (KWS).

Keyword Spotting Speech Recognition

Deep Multiscale Model Learning

no code implementations13 Jun 2018 Yating Wang, Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev, Min Wang

Numerical results show that using deep learning and multiscale models, we can improve the forward models, which are conditioned to the available data.

DRPose3D: Depth Ranking in 3D Human Pose Estimation

no code implementations23 May 2018 Min Wang, Xipeng Chen, Wentao Liu, Chen Qian, Liang Lin, Lizhuang Ma

In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation.

3D Human Pose Estimation 3D Pose Estimation

YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations

no code implementations IJCNLP 2017 Min Wang, Qingxun Liu, Peng Ding, Yongbin Li, Xiaobing Zhou

In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.

Question Answering

Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure

1 code implementation15 Aug 2016 Min Wang, Baoyuan Liu, Hassan Foroosh

A topological subdivisioning is adopted to reduce the connection between the input channels and output channels.

Sparse Convolutional Neural Networks

no code implementations CVPR 2015 Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, Marianna Pensky

Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity.

Image Classification Object Detection

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