Search Results for author: Jun Li

Found 134 papers, 31 papers with code

Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

no code implementations27 Jun 2022 Jun Li, Yushan Zheng, Kun Wu, Jun Shi, Fengying Xie, Zhiguo Jiang

In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis.

Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification

1 code implementation27 Jun 2022 Yushan Zheng, Jun Li, Jun Shi, Fengying Xie, Zhiguo Jiang

Transformer has been widely used in histopathology whole slide image (WSI) classification for the purpose of tumor grading, prognosis analysis, etc.

A Self-Guided Framework for Radiology Report Generation

no code implementations19 Jun 2022 Jun Li, Shibo Li, Ying Hu, Huiren Tao

Moreover, SGF successfully improves the accuracy and length of medical report generation by incorporating a similarity comparison mechanism that imitates the process of human self-improvement through compar-ative practice.

Image Captioning Medical Report Generation

Design of optical voltage sensor based on electric field regulation and rotating isomerism electrode

no code implementations14 Jun 2022 Jun Li, Yifan Lin, Nan Xie

This technology could shift the measured signal frequency band from near 50 Hz moved to several kilometer Hz, so as to make the output signal avoid the interference from low-frequency temperature drift, stress birefringence and vibration, leading to higher stability and reliability.

CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers

no code implementations30 May 2022 Jun Li, Shangwei Guo, Shaokun Han

Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details.

Point Cloud Completion

Providing Location Information at Edge Networks: A Federated Learning-Based Approach

no code implementations17 May 2022 Xin Cheng, Tingting Liu, Feng Shu, Chuan Ma, Jun Li, Jiangzhou Wang

Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication cost.

Edge-computing Federated Learning +1

A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model

no code implementations ICLR 2022 Jianwen Xie, Yaxuan Zhu, Jun Li, Ping Li

Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow.

CoDo: Contrastive Learning with Downstream Background Invariance for Detection

no code implementations10 May 2022 Bing Zhao, Jun Li, Hong Zhu

To bridge the performance gap, we propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo).

Contrastive Learning Data Augmentation +5

Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives

no code implementations18 Apr 2022 Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, Antonio J Plaza

Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.

Change Detection Image Classification +3

A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification

no code implementations9 Apr 2022 Heng-Chao Li, Wen-Shuai Hu, Wei Li, Jun Li, Qian Du, Antonio Plaza

The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing.

Transfer Learning

MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation

1 code implementation7 Apr 2022 Ting Zhang, Jun Li, Yi Zhao, Nan Chen, Han Zhou, Hongtao Xu, Zihao Guan, Changcai Yang, Lanyan Xue, Riqing Chen, Lifang Wei

The proposed network structure retains three layers the essential structure of U-Net, in which the atrous convolution combining the multi-kernel pooling blocks are designed to obtain more contextual information.

Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning

no code implementations3 Apr 2022 Tae-Kyoung Kim, Yo-Seb Jeon, Jun Li, Nima Tavangaran, H. Vincent Poor

Data-aided channel estimation is a promising solution to improve channel estimation accuracy by exploiting data symbols as pilot signals for updating an initial channel estimate.


Energy-Efficient IRS-Aided NOMA Beamforming for 6G Wireless Communications

no code implementations30 Mar 2022 Asim Ihsan, Wen Chen, Muhammad Asif, Wali Ullah Khan, Jun Li

This manuscript presents an energy-efficient alternating optimization framework based on intelligent reflective surfaces (IRS) aided non-orthogonal multiple access beamforming (NOMA-BF) system for 6G wireless communications.

Federated Learning-Based Localization with Heterogeneous Fingerprint Database

no code implementations29 Mar 2022 Xin Cheng, Chuan Ma, Jun Li, Haiwei Song, Feng Shu, Jiangzhou Wang

Fingerprint-based localization plays an important role in indoor location-based services, where the position information is usually collected in distributed clients and gathered in a centralized server.

Federated Learning

Nested Collaborative Learning for Long-Tailed Visual Recognition

1 code implementation CVPR 2022 Jun Li, Zichang Tan, Jun Wan, Zhen Lei, Guodong Guo

NCL consists of two core components, namely Nested Individual Learning (NIL) and Nested Balanced Online Distillation (NBOD), which focus on the individual supervised learning for each single expert and the knowledge transferring among multiple experts, respectively.

Image Classification Long-tailed Learning +1

Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation

1 code implementation CVPR 2022 Jinchao Yang, Fei Guo, Shuo Chen, Jun Li, Jian Yang

Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product.

Style Transfer

Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion

no code implementations18 Mar 2022 Zhiqiang Yan, Xiang Li, Kun Wang, Zhenyu Zhang, Jun Li, Jian Yang

Specifically, during pre-training, we simultaneously cover up patches of the panoramic RGB image and sparse depth by shared random mask, then reconstruct the sparse depth in the masked regions.

Depth Completion Transfer Learning

Universal Segmentation of 33 Anatomies

no code implementations4 Mar 2022 Pengbo Liu, Yang Deng, Ce Wang, Yuan Hui, Qian Li, Jun Li, Shiwei Luo, Mengke Sun, Quan Quan, Shuxin Yang, You Hao, Honghu Xiao, Chunpeng Zhao, Xinbao Wu, S. Kevin Zhou

Firstly, while it is ideal to learn such a model from a large-scale, fully-annotated dataset, it is practically hard to curate such a dataset.

Medical Image Segmentation Semantic Segmentation

MixCL: Pixel label matters to contrastive learning

no code implementations4 Mar 2022 Jun Li, Quan Quan, S. Kevin Zhou

It is essential for medical image analysis, which is often notorious for its lack of annotations.

Contrastive Learning Medical Image Segmentation +1

A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients

no code implementations21 Feb 2022 Zhijun Zeng, Zhen Hou, Ting Li, Lei Deng, Jianguo Hou, Xinran Huang, Jun Li, Meirou Sun, Yunhan Wang, Qiyu Wu, Wenhao Zheng, Hua Jiang, Qi Wang

We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model.

Vertical Federated Learning: Challenges, Methodologies and Experiments

no code implementations9 Feb 2022 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Sha Wei, Fan Wu, Guihai Chen, Thilina Ranbaduge

As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.

Federated Learning

Locally Random P-adic Alloy Codes with Channel Coding Theorems for Distributed Coded Tensors

no code implementations7 Feb 2022 Pedro Soto, Haibin Guan, Jun Li

We introduce a new metric for analysis called the typical recovery threshold, which focuses on the most likely event and provide a novel construction of distributed coded tensor operations which are optimal with this measure.

Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent

no code implementations31 Jan 2022 Pedro Soto, Ilia Ilmer, Haibin Guan, Jun Li

Coded distributed computation has become common practice for performing gradient descent on large datasets to mitigate stragglers and other faults.

Temporal Transformer Networks with Self-Supervision for Action Recognition

no code implementations14 Dec 2021 Yongkang Zhang, Jun Li, Guoming Wu, Han Zhang, Zhiping Shi, Zhaoxun Liu, Zizhang Wu, Na Jiang

The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of "random batch random channel" to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model.

Action Recognition

Which images to label for few-shot medical landmark detection?

no code implementations CVPR 2022 Quan Quan, Qingsong Yao, Jun Li, S. Kevin Zhou

We herein propose a novel Sample Choosing Policy (SCP) to select "the most worthy" images for annotation, in the context of few-shot medical landmark detection.

Few-Shot Learning

CT-block: a novel local and global features extractor for point cloud

no code implementations30 Nov 2021 Shangwei Guo, Jun Li, Zhengchao Lai, Xiantong Meng, Shaokun Han

Meanwhile, the transformer-branch performs offset-attention process on the whole point cloud to extract the global feature.

Point Cloud Classification

Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution

no code implementations17 Nov 2021 Xi Cheng, Jun Li, Qiang Dai, ZhenYong Fu, Jian Yang

In our SF-SIM, we propose a noise estimator which can effectively suppress the noise in the image and enable our method to work under the low light and short exposure environment, without the need for stacking multiple frames for non-local denoising.

Denoising Super-Resolution

RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

no code implementations27 Oct 2021 Runmin Cong, Yumo Zhang, Leyuan Fang, Jun Li, Yao Zhao, Sam Kwong

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs.

object-detection Object Detection +2

$f$-Divergence Thermodynamic Variational Objective: a Deformed Geometry Perspective

no code implementations29 Sep 2021 Jun Li, Ping Li

In this paper, we propose a $f$-divergence Thermodynamic Variational Objective ($f$-TVO).

Variational Inference

Constructing Orthogonal Convolutions in an Explicit Manner

no code implementations ICLR 2022 Tan Yu, Jun Li, Yunfeng Cai, Ping Li

A convolution layer with an orthogonal Jacobian matrix is 1-Lipschitz in the 2-norm, making the output robust to the perturbation in input.

Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning

no code implementations29 Sep 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.


Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration

1 code implementation ICCV 2021 Haobo Jiang, Yaqi Shen, Jin Xie, Jun Li, Jianjun Qian, Jian Yang

Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations.

Point Cloud Registration

Elevation Angle-Dependent 3D Trajectory Design for Aerial RIS-aided Communication

no code implementations23 Aug 2021 Yifan Liu, Bin Duo, Qingqing Wu, Xiaojun Yuan, Jun Li, Yonghui Li

This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange.

6G Internet of Things: A Comprehensive Survey

no code implementations11 Aug 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor

The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.

Autonomous Driving

An optical biomimetic eyes with interested object imaging

no code implementations8 Aug 2021 Jun Li, Shimei Chen, Shangyuan Wang, Miao Lei, Xiaofang Dai, Chuangxue Liang, Kunyuan Xu, Shuxin Lin, Yuhui Li, Yuer Fan, Ting Zhong

We presented an optical system to perform imaging interested objects in complex scenes, like the creature easy see the interested prey in the hunt for complex environments.

object-detection Object Detection +2

RigNet: Repetitive Image Guided Network for Depth Completion

no code implementations29 Jul 2021 Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Baobei Xu, Jun Li, Jian Yang

Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this task.

Depth Completion Depth Estimation

Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks

no code implementations23 Jul 2021 Zhendong Li, Wen Chen, Huanqing Cao, Hongying Tang, Kunlun Wang, Jun Li

Aiming at the limited battery capacity of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the wireless energy transmission efficiency and coverage area of the UAV SWIPT networks.

Low-Latency Federated Learning over Wireless Channels with Differential Privacy

no code implementations20 Jun 2021 Kang Wei, Jun Li, Chuan Ma, Ming Ding, Cailian Chen, Shi Jin, Zhu Han, H. Vincent Poor

Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.

Federated Learning

IPS300+: a Challenging Multimodal Dataset for Intersection Perception System

no code implementations5 Jun 2021 Huanan Wang, Xinyu Zhang, Jun Li, Zhiwei Li, Lei Yang, Shuyue Pan, Yongqiang Deng

Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multimodal dataset for the intersection perception task.

Federated Learning for Industrial Internet of Things in Future Industries

no code implementations31 May 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, H. Vincent Poor

The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.

Federated Learning

Dynamic network analysis improves protein 3D structural classification

no code implementations14 May 2021 Khalique Newaz, Jacob Piland, Patricia L. Clark, Scott J. Emrich, Jun Li, Tijana Milenkovic

Here, we propose for the first time a way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches).


An Extension of BIM Using AI: a Multi Working-Machines Pathfinding Solution

no code implementations14 May 2021 Yusheng Xiang, Kailun Liu, Tianqing Su, Jun Li, Shirui Ouyang, Samuel S. Mao, Marcus Geimer

In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime.

Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design

1 code implementation10 May 2021 Chuan Ma, Jun Li, Ming Ding, Kang Wei, Wen Chen, H. Vincent Poor

Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.

Federated Learning

Lite-FPN for Keypoint-based Monocular 3D Object Detection

1 code implementation1 May 2021 Lei Yang, Xinyu Zhang, Li Wang, Minghan Zhu, Jun Li

3D object detection with a single image is an essential and challenging task for autonomous driving.

Autonomous Driving Monocular 3D Object Detection +1

ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification

no code implementations25 Apr 2021 Jinping Wang, Xiaojun Tan, JianHuang Lai, Jun Li, Canqun Xiang

Based on this observation, this paper proposes an adaptive spatial pattern capsule network (ASPCNet) architecture by developing an adaptive spatial pattern (ASP) unit, that can rotate the sampling location of convolutional kernels on the basis of an enlarged receptive field.

General Classification Hyperspectral Image Classification

Federated Learning for Internet of Things: A Comprehensive Survey

no code implementations16 Apr 2021 Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).

Federated Learning

Cross-Validated Tuning of Shrinkage Factors for MVDR Beamforming Based on Regularized Covariance Matrix Estimation

no code implementations5 Apr 2021 Lei Xie, Zishu He, Jun Tong, Jun Li, Jiangtao Xi

We propose leave-one-out cross-validation (LOOCV) choices for the shrinkage factors to optimize the beamforming performance, referred to as $\text{S}^2$CM-CV and STE-CV.

Action Shuffle Alternating Learning for Unsupervised Action Segmentation

no code implementations CVPR 2021 Jun Li, Sinisa Todorovic

Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding.

Action Segmentation Self-Supervised Learning

Potential Convolution: Embedding Point Clouds into Potential Fields

no code implementations5 Apr 2021 Dengsheng Chen, Haowen Deng, Jun Li, Duo Li, Yao Duan, Kai Xu

In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution.

3D Shape Classification Scene Segmentation

Anchor-Constrained Viterbi for Set-Supervised Action Segmentation

no code implementations CVPR 2021 Jun Li, Sinisa Todorovic

This paper is about action segmentation under weak supervision in training, where the ground truth provides only a set of actions present, but neither their temporal ordering nor when they occur in a training video.

Action Segmentation

A novel multimodal fusion network based on a joint coding model for lane line segmentation

no code implementations20 Mar 2021 Zhenhong Zou, Xinyu Zhang, Huaping Liu, Zhiwei Li, Amir Hussain, Jun Li

There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation.

Regularized Covariance Estimation for Polarization Radar Detection in Compound Gaussian Sea Clutter

no code implementations17 Mar 2021 Lei Xie, Zishu He, Jun Tong, Tianle Liu, Jun Li, Jiangtao Xi

This paper investigates regularized estimation of Kronecker-structured covariance matrices (CM) for polarization radar in sea clutter scenarios where the data are assumed to follow the complex, elliptically symmetric (CES) distributions with a Kronecker-structured CM.

S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection

1 code implementation15 Mar 2021 Li Wang, Chenfei Wang, Xinyu Zhang, Tianwei Lan, Jun Li

3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control.

3D Object Detection Autonomous Vehicles +1

Roles of the Narrow Electronic Band near the Fermi Level in 1$T$-TaS$_2$-Related Layered Materials

no code implementations11 Mar 2021 Chenhaoping Wen, Jingjing Gao, Yuan Xie, Qing Zhang, Pengfei Kong, Jinghui Wang, Yilan Jiang, Xuan Luo, Jun Li, Wenjian Lu, Yu-Ping Sun, Shichao Yan

4$H_{\rm b}$-TaS$_2$ is a superconducting compound with alternating 1$T$-TaS$_2$ and 1$H$-TaS$_2$ layers, where the 1$H$-TaS$_2$ layer has weak charge density wave (CDW) pattern and reduces the CDW coupling between the adjacent 1$T$-TaS$_2$ layers.

Mesoscale and Nanoscale Physics Materials Science

Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy

no code implementations3 Feb 2021 James Mou, Jun Li

Our results show a strong dependency of word accuracy on the Number of Filters of convolutional layers.

Automatic Speech Recognition

Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization

no code implementations28 Jan 2021 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Yo-Seb Jeon, H. Vincent Poor

An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).

Federated Learning Model Poisoning

Solving localized wave solutions of the derivative nonlinear Schrodinger equation using an improved PINN method

no code implementations21 Jan 2021 Juncai Pu, Jun Li, Yong Chen

On the bases of the improved method, the effects for different numbers of initial points sampled, residual collocation points sampled, network layers, neurons per hidden layer on the second order genuine rational soliton solution dynamics of the DNLS are considered, and the relevant analysis when the locally adaptive activation function chooses different initial values of scalable parameters are also exhibited in the simulation of the two-order rogue wave solution.

Pattern Formation and Solitons Exactly Solvable and Integrable Systems

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

no code implementations18 Jan 2021 Jun Li, Yumeng Shao, Kang Wei, Ming Ding, Chuan Ma, Long Shi, Zhu Han, H. Vincent Poor

Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.

Federated Learning

Robust Dynamical Decoupling for the Manipulation of a Spin Network via a Single Spin

no code implementations11 Jan 2021 Xiaodong Yang, Yunrui Ge, Bo Zhang, Jun Li

High-fidelity control of quantum systems is crucial for quantum information processing, but is often limited by perturbations from the environment and imperfections in the applied control fields.

Quantum Physics

Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models

1 code implementation16 Dec 2020 Pengbo Liu, Hu Han, Yuanqi Du, Heqin Zhu, Yinhao Li, Feng Gu, Honghu Xiao, Jun Li, Chunpeng Zhao, Li Xiao, Xinbao Wu, S. Kevin Zhou

Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.

Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients

no code implementations2 Dec 2020 Jun Li, Yumeng Shao, Ming Ding, Chuan Ma, Kang Wei, Zhu Han, H. Vincent Poor

The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.

Federated Learning

Scalable Federated Learning over Passive Optical Networks

no code implementations29 Oct 2020 Jun Li, Lei Chen, Jiajia Chen

Two-step aggregation is introduced to facilitate scalable federated learning (SFL) over passive optical networks (PONs).

Networking and Internet Architecture

Reconstruction of Quantitative Susceptibility Maps from Phase of Susceptibility Weighted Imaging with Cross-Connected $Ψ$-Net

no code implementations12 Oct 2020 Zhiyang Lu, Jun Li, Zheng Li, Hongjian He, Jun Shi

In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $\Psi$-Net (C$\Psi$-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing.

When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm

no code implementations20 Sep 2020 Chuan Ma, Jun Li, Ming Ding, Long Shi, Taotao Wang, Zhu Han, H. Vincent Poor

Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.

Networking and Internet Architecture

A Short Review on Data Modelling for Vector Fields

no code implementations1 Sep 2020 Jun Li, Wanrong Hong, Yusheng Xiang

On the application side, vector fields are an extremely useful type of data in empirical sciences, as well as signal processing, e. g. non-parametric transformations of 3D point clouds using 3D vector fields, the modelling of the fluid flow in earth science, and the modelling of physical fields.

Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon

no code implementations26 Aug 2020 Teng Liu, Hong Wang, Bing Lu, Jun Li, Dongpu Cao

Decision-making strategy for autonomous vehicles de-scribes a sequence of driving maneuvers to achieve a certain navigational mission.

Autonomous Vehicles Decision Making +1

LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

1 code implementation3 Aug 2020 Qiao Liu, Xin Li, Zhenyu He, Chenglong Li, Jun Li, Zikun Zhou, Di Yuan, Jing Li, Kai Yang, Nana Fan, Feng Zheng

We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.

Thermal Infrared Object Tracking

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

1 code implementation4 Jul 2020 Chuan Ma, Jun Li, Ming Ding, Bo Liu, Kang Wei, Jian Weng, H. Vincent Poor

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.

An Empirical Comparison of Unsupervised Constituency Parsing Methods

no code implementations ACL 2020 Jun Li, Yifan Cao, Jiong Cai, Yong Jiang, Kewei Tu

Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations.

Constituency Parsing

AReLU: Attention-based Rectified Linear Unit

1 code implementation24 Jun 2020 Dengsheng Chen, Jun Li, Kai Xu

Adding the attention module with a rectified linear unit (ReLU) results in an amplification of positive elements and a suppression of negative ones, both with learned, data-adaptive parameters.

Meta-Learning Transfer Learning

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

6 code implementations NeurIPS 2020 Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang

Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations.

Classification Dense Object Detection +2

Reconfigurable Intelligent Surface (RIS)-Enhanced Two-Way OFDM Communications

no code implementations5 May 2020 Chandan Pradhan, Ang Li, Lingyang Song, Jun Li, Branka Vucetic, Yonghui Li

In this paper, we focus on the reconfigurable intelligent surface (RIS)-enhanced two-way device-to-device (D2D) multi-pair orthogonal-frequency-division-multiplexing (OFDM) communication systems.

DNN-aided Read-voltage Threshold Optimization for MLC Flash Memory with Finite Block Length

no code implementations11 Apr 2020 Cheng Wang, Kang Wei, Lingjun Kong, Long Shi, Zhen Mei, Jun Li, Kui Cai

The error correcting performance of multi-level-cell (MLC) NAND flash memory is closely related to the block length of error correcting codes (ECCs) and log-likelihood-ratios (LLRs) of the read-voltage thresholds.

Learnable Subspace Clustering

1 code implementation9 Apr 2020 Jun Li, Hongfu Liu, Zhiqiang Tao, Handong Zhao, Yun Fu

This paper studies the large-scale subspace clustering (LSSC) problem with million data points.

A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems

no code implementations18 Mar 2020 Yo-Seb Jeon, Mohammad Mohammadi Amiri, Jun Li, H. Vincent Poor

One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices.

Compressive Sensing Federated Learning +1

User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization

no code implementations29 Feb 2020 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Hang Su, Bo Zhang, H. Vincent Poor

According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.

Federated Learning Privacy Preserving

Set-Constrained Viterbi for Set-Supervised Action Segmentation

no code implementations CVPR 2020 Jun Li, Sinisa Todorovic

This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering.

Action Segmentation Multiple Instance Learning

Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning

no code implementations22 Feb 2020 Jun Li, Zhichao Xing, Weibin Zhang, Yan Lin, Feng Shu

Vehicle tracking has become one of the key applications of wireless sensor networks (WSNs) in the fields of rescue, surveillance, traffic monitoring, etc.


Face Hallucination with Finishing Touches

no code implementations9 Feb 2020 Yang Zhang, Ivor W. Tsang, Jun Li, Ping Liu, Xiaobo Lu, Xin Yu

The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i. e., fine-grained facial components, to attain a frontal HR face image with authentic details.

Face Hallucination Face Recognition

Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation

no code implementations CVPR 2020 Dengsheng Chen, Jun Li, Zheng Wang, Kai Xu

To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.

3D Shape Representation Generating 3D Point Clouds

UAV-Enabled Confidential Data Collection in Wireless Networks

no code implementations3 Jan 2020 Xiaobo Zhou, Shihao Yan, Min Li, Jun Li, Feng Shu

This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs.

Naive Gabor Networks for Hyperspectral Image Classification

no code implementations9 Dec 2019 Chenying Liu, Jun Li, Lin He, Antonio J. Plaza, Shutao Li, Bo Li

Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase.

Classification General Classification +1

Curvilinear Distance Metric Learning

1 code implementation NeurIPS 2019 Shuo Chen, Lei Luo, Jian Yang, Chen Gong, Jun Li, Heng Huang

To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines.

Metric Learning

Lifelong Spectral Clustering

no code implementations27 Nov 2019 Gan Sun, Yang Cong, Qianqian Wang, Jun Li, Yun Fu

As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks.

Symplectic $(-2)$-spheres and the symplectomorphism group of small rational 4-manifolds, II

no code implementations25 Nov 2019 Jun Li, Tian-Jun Li, Weiwei Wu

For $(\mathbb{C} P^2 \# 5{\overline {\mathbb{C} P^2}},\omega)$, let $N_{\omega}$ be the number of $(-2)$-symplectic spherical homology classes. We completely determine the Torelli symplectic mapping class group (Torelli SMCG): the Torelli SMCG is trivial if $N_{\omega}>8$; it is $\pi_0(Diff^+(S^2, 5))$ if $N_{\omega}=0$ (by Paul Seidel and Jonathan Evans); it is $\pi_0(Diff^+(S^2, 4))$ in the remaining case.

Symplectic Geometry

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

no code implementations1 Nov 2019 Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farokhi Farhad, Shi Jin, Tony Q. S. Quek, H. Vincent Poor

Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.

Federated Learning Privacy Preserving

LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution

no code implementations25 Oct 2019 Bin Sun, Jun Li, Ming Shao, Yun Fu

To reduce the computation and memory costs, we propose a novel lightweight deep learning module by low-rank pointwise residual (LPR) convolution, called LPRNet.

Face Alignment Image Classification +1

Bandwidth Slicing to Boost Federated Learning in Edge Computing

no code implementations24 Oct 2019 Jun Li, Xiaoman Shen, Lei Chen, Jiajia Chen

Bandwidth slicing is introduced to support federated learning in edge computing to assure low communication delay for training traffic.

Edge-computing Federated Learning

Weighted graphlets and deep neural networks for protein structure classification

no code implementations7 Oct 2019 Hongyu Guo, Khalique Newaz, Scott Emrich, Tijana Milenkovic, Jun Li

We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks.

Classification General Classification

Weakly Supervised Energy-Based Learning for Action Segmentation

1 code implementation ICCV 2019 Jun Li, Peng Lei, Sinisa Todorovic

This paper is about labeling video frames with action classes under weak supervision in training, where we have access to a temporal ordering of actions, but their start and end frames in training videos are unknown.

Video Segmentation Video Semantic Segmentation +1

Variable selection with false discovery rate control in deep neural networks

1 code implementation17 Sep 2019 Zixuan Song, Jun Li

Deep neural networks (DNNs) are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability.

Variable Selection

On Safeguarding Privacy and Security in the Framework of Federated Learning

no code implementations14 Sep 2019 Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).

Networking and Internet Architecture

Cross-X Learning for Fine-Grained Visual Categorization

no code implementations ICCV 2019 Wei Luo, Xitong Yang, Xianjie Mo, Yuheng Lu, Larry S. Davis, Jun Li, Jian Yang, Ser-Nam Lim

Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation.

Ranked #9 on Fine-Grained Image Classification on NABirds (using extra training data)

Fine-Grained Image Classification Fine-Grained Visual Categorization

Learning Part Generation and Assembly for Structure-aware Shape Synthesis

no code implementations16 Jun 2019 Jun Li, Chengjie Niu, Kai Xu

Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet.

Shakeout: A New Approach to Regularized Deep Neural Network Training

1 code implementation13 Apr 2019 Guoliang Kang, Jun Li, DaCheng Tao

Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training.

Model Compression

Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet

no code implementations3 Mar 2019 Jun Li, Xiaozhu Lin, Hui Che, Hao Li, Xiaohua Qian

To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fuses intra-slice information and inter-slice probabilistic maps into a local 3D hybrid regularization scheme, which is followed by bi-directional recurrent network optimization.

Pancreas Segmentation

A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation

no code implementations3 Mar 2019 Hao Li, Jun Li, Xiaozhu Lin, Xiaohua Qian

The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods.

Pancreas Segmentation

Privacy Preserving Location Data Publishing: A Machine Learning Approach

no code implementations24 Feb 2019 Sina Shaham, Ming Ding, Bo Liu, Shuping Dang, Zihuai Lin, Jun Li

By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.

Multiple Sequence Alignment Privacy Preserving

Deep Discriminative Representation Learning with Attention Map for Scene Classification

no code implementations21 Feb 2019 Jun Li, Daoyu Lin, Yang Wang, Guangluan Xu, Chibiao Ding

However, most recent approaches to remote sensing scene classification are based on Convolutional Neural Networks (CNNs).

Classification Face Recognition +3

On Learning and Learned Data Representation by Capsule Networks

no code implementations8 Oct 2018 Ancheng Lin, Jun Li, Zhenyuan Ma

In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks.

GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation

no code implementations27 Sep 2018 Guoshuai Zhao, Jun Li, Lu Wang, Xueming Qian, Yun Fu

In this paper, we propose a Graph-Sequence-to-Sequence(GraphSeq2Seq) model to fuse the dependency graph among words into the traditional Seq2Seq framework.

Image Captioning Machine Translation +3

Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN

no code implementations10 Jun 2018 Yiqi Yan, Lei Zhang, Jun Li, Wei Wei, Yanning Zhang

Different from traditional hyperspectral super-resolution approaches that focus on improving the spatial resolution, spectral super-resolution aims at producing a high-resolution hyperspectral image from the RGB observation with super-resolution in spectral domain.

Spectral Reconstruction Spectral Super-Resolution +1

Predictive Local Smoothness for Stochastic Gradient Methods

no code implementations ICLR 2019 Jun Li, Hongfu Liu, Bineng Zhong, Yue Wu, Yun Fu

To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS).

Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model

no code implementations16 May 2018 Sina Shaham, Ming Ding, Bo Liu, Zihuai Lin, Jun Li

In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.

Generative Steganography by Sampling

no code implementations26 Apr 2018 Jia Liu, Yu Lei, Yan Ke, Jun Li, Min-qing Zhang, Xiaoyuan Yan

In this paper, a new data-driven information hiding scheme called generative steganography by sampling (GSS) is proposed.

Image Inpainting

Im2Struct: Recovering 3D Shape Structure from a Single RGB Image

no code implementations CVPR 2018 Chengjie Niu, Jun Li, Kai Xu

We propose to recover 3D shape structures from single RGB images, where structure refers to shape parts represented by cuboids and part relations encompassing connectivity and symmetry.

One-Class Adversarial Nets for Fraud Detection

1 code implementation5 Mar 2018 Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu

Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.

Fraud Detection

Adversarial Metric Learning

no code implementations9 Feb 2018 Shuo Chen, Chen Gong, Jian Yang, Xiang Li, Yang Wei, Jun Li

In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs.

Metric Learning

Clustering with Outlier Removal

no code implementations5 Jan 2018 Hongfu Liu, Jun Li, Yue Wu, Yun Fu

Then an objective function based Holoentropy is designed to enhance the compactness of each cluster with a few outliers removed.

Outlier Detection

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

4 code implementations4 Jan 2018 Xi-Wei Yao, Hengyan Wang, Zeyang Liao, Ming-Cheng Chen, Jian Pan, Jun Li, Kechao Zhang, Xingcheng Lin, Zhehui Wang, Zhihuang Luo, Wenqiang Zheng, Jianzhong Li, Meisheng Zhao, Xinhua Peng, Dieter Suter

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power.

Quantum Physics

Spectrum-based deep neural networks for fraud detection

no code implementations3 Jun 2017 Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu

Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible.

Fraud Detection

Dense Transformer Networks

1 code implementation24 May 2017 Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, Shuiwang Ji

The proposed dense transformer modules are differentiable, thus the entire network can be trained.

Semantic Segmentation

GRASS: Generative Recursive Autoencoders for Shape Structures

no code implementations5 May 2017 Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, Leonidas Guibas

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.

ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering

no code implementations CVPR 2016 Zhang Zhang, Kaiqi Huang, Tieniu Tan, Peipei Yang, Jun Li

For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data.

graph construction Motion Segmentation +2

Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps

no code implementations29 Apr 2016 Biyun Sheng, Chunhua Shen, Guosheng Lin, Jun Li, Wankou Yang, Changyin Sun

Crowd counting is an important task in computer vision, which has many applications in video surveillance.

Crowd Counting

Sparse Deep Stacking Network for Image Classification

no code implementations5 Jan 2015 Jun Li, Heyou Chang, Jian Yang

Luckily, a simplified neural network module (SNNM) has been proposed to directly learn the discriminative dictionaries for avoiding the expensive inference.

Classification General Classification +1

Unsupervised Pretraining Encourages Moderate-Sparseness

no code implementations20 Dec 2013 Jun Li, Wei Luo, Jian Yang, Xiao-Tong Yuan

It is well known that direct training of deep neural networks will generally lead to poor results.

Cannot find the paper you are looking for? You can Submit a new open access paper.