Search Results for author: Fei-Yue Wang

Found 22 papers, 5 papers with code

SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation

1 code implementation CVPR 2021 Siqi Fan, Qiulei Dong, Fenghua Zhu, Yisheng Lv, Peijun Ye, Fei-Yue Wang

For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud.

3D Semantic Segmentation Point Cloud Segmentation

Drill the Cork of Information Bottleneck by Inputting the Most Important Data

no code implementations15 May 2021 Xinyu Peng, Jiawei Zhang, Fei-Yue Wang, Li Li

As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase.

Deep Q Learning from Dynamic Demonstration with Behavioral Cloning

no code implementations1 Jan 2021 Xiaoshuang Li, Junchen Jin, Xiao Wang, Fei-Yue Wang

This study proposes a novel approach integrating deep Q learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC), which includes a supervised learning technique of instructing a DRL model to enhance its performance.

OpenAI Gym Q-Learning

FCM-RDpA: TSK Fuzzy Regression Model Construction Using Fuzzy C-Means Clustering, Regularization, DropRule, and Powerball AdaBelief

1 code implementation30 Nov 2020 Zhenhua Shi, Dongrui Wu, Chenfeng Guo, Changming Zhao, Yuqi Cui, Fei-Yue Wang

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed.

Dynamic Fusion based Federated Learning for COVID-19 Detection

no code implementations22 Sep 2020 Weishan Zhang, Tao Zhou, Qinghua Lu, Xiao Wang, Chunsheng Zhu, Haoyun Sun, Zhipeng Wang, Sin Kit Lo, Fei-Yue Wang

To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.

Decision Making Federated Learning +1

Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression

no code implementations8 Sep 2020 Jianji Wang, Qi Liu, Shupei Zhang, Nanning Zheng, Fei-Yue Wang

By the proposed method, the computational complexity is reduced from $O(\frac{1}{6}{k^3}+mk^2+mkd)$ to $O(\frac{1}{6}{k^3}+\frac{1}{2}mk^2)$ for each candidate subset in sparse regression.

Convolutional Ordinal Regression Forest for Image Ordinal Estimation

no code implementations7 Aug 2020 Haiping Zhu, Hongming Shan, Yuheng Zhang, Lingfu Che, Xiaoyang Xu, Junping Zhang, Jianbo Shi, Fei-Yue Wang

We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.

Age Estimation

Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving

no code implementations26 Jul 2020 Teng Liu, Yang Xing, Long Chen, Dongpu Cao, Fei-Yue Wang

The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles.

Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications

no code implementations21 Jul 2020 Teng Liu, Xing Yang, Hong Wang, Xiaolin Tang, Long Chen, Huilong Yu, Fei-Yue Wang

The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle.

Adaptive and Azimuth-Aware Fusion Network of Multimodal Local Features for 3D Object Detection

no code implementations10 Oct 2019 Yonglin Tian, Kunfeng Wang, Yuang Wang, Yulin Tian, Zilei Wang, Fei-Yue Wang

We adopt different modalities of LiDAR data to generate richer features and present an adaptive and azimuth-aware network to aggregate local features from image, bird's eye view maps and point cloud.

3D Object Detection Region Proposal

A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

no code implementations13 Mar 2019 Xiuqin Shang, Dayong Shen, Fei-Yue Wang, Timo R. Nyberg

Firstly the constructive procedure creates a set of lays in sequence, and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set.

Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications

no code implementations12 Mar 2019 Chao Gou, Tianyu Shen, Wenbo Zheng, Huadan Xue, Hui Yu, Qiang Ji, Zhengyu Jin, Fei-Yue Wang

Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge.

Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling

no code implementations11 Mar 2019 Xinyu Peng, Li Li, Fei-Yue Wang

Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning.

Speech Recognition Stochastic Optimization

Mutual Clustering on Comparative Texts via Heterogeneous Information Networks

no code implementations9 Mar 2019 Jianping Cao, Senzhang Wang, Danyan Wen, Zhaohui Peng, Philip S. Yu, Fei-Yue Wang

HINT first models multi-sourced texts (e. g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts.

Text Clustering Transfer Learning

An Efficient Deep Reinforcement Learning Model for Urban Traffic Control

1 code implementation6 Aug 2018 Yilun Lin, Xingyuan Dai, Li Li, Fei-Yue Wang

Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult.

M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance

no code implementations14 Feb 2018 Kunfeng Wang, Chao Gou, Fei-Yue Wang

Secondly, multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation) are extracted by comparing the input frame with the background model, and a multi-source learning strategy is designed to online estimate the probability distributions for both foreground and background.

Scene-Specific Pedestrian Detection Based on Parallel Vision

1 code implementation23 Dec 2017 Wenwen Zhang, Kunfeng Wang, Hua Qu, Jihong Zhao, Fei-Yue Wang

In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes.

Object Detection Pedestrian Detection

Training and Testing Object Detectors with Virtual Images

no code implementations22 Dec 2017 Yonglin Tian, Xuan Li, Kunfeng Wang, Fei-Yue Wang

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data.

The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research

no code implementations22 Dec 2017 Xuan Li, Kunfeng Wang, Yonglin Tian, Lan Yan, Fei-Yue Wang

As a result, we present a viable implementation pipeline for constructing large-scale artificial scenes for traffic vision research.

Instance Segmentation Object Tracking +3

DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction

no code implementations11 Jul 2017 Xingyuan Dai, Rui Fu, Yilun Lin, Li Li, Fei-Yue Wang

Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction.

Time Series

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