Search Results for author: Jun Shi

Found 45 papers, 12 papers with code

Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective

no code implementations18 Nov 2023 Yixiao Yang, Ran Tao, Kaixuan Wei, Jun Shi

The realm of classical phase retrieval concerns itself with the arduous task of recovering a signal from its Fourier magnitude measurements, which are fraught with inherent ambiguities.


Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks

1 code implementation ICCV 2023 Sixiang Chen, Tian Ye, Jinbin Bai, ErKang Chen, Jun Shi, Lei Zhu

In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image.

Rain Removal

Multi-Scale Prototypical Transformer for Whole Slide Image Classification

no code implementations5 Jul 2023 Saisai Ding, Jun Wang, Juncheng Li, Jun Shi

The PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture.

Classification Image Classification +1

H-DenseFormer: An Efficient Hybrid Densely Connected Transformer for Multimodal Tumor Segmentation

1 code implementation4 Jul 2023 Jun Shi, Hongyu Kan, Shulan Ruan, Ziqi Zhu, Minfan Zhao, Liang Qiao, Zhaohui Wang, Hong An, Xudong Xue

In this paper, we propose a hybrid densely connected network for tumor segmentation, named H-DenseFormer, which combines the representational power of the Convolutional Neural Network (CNN) and the Transformer structures.

Tumor Segmentation

Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer

no code implementations26 Jun 2023 Xiangneng Gao, Shulan Ruan, Jun Shi, Guoqing Hu, Wei Wei

To this end, in this paper, we propose a novel Multi-View Attention Learning (MuVAL) method for residual disease prediction, which focuses on the comprehensive learning of 3D Computed Tomography (CT) images in a multi-view manner.

Computed Tomography (CT) Decision Making +1

Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images

no code implementations12 Jun 2023 Jian Wang, Liang Qiao, Shichong Zhou, Jin Zhou, Jun Wang, Juncheng Li, Shihui Ying, Cai Chang, Jun Shi

To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to enhance diagnostic accuracy of the ultrasound-based CAD for breast cancers.

Lesion Detection Weakly-supervised Learning

Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification

no code implementations25 May 2023 Saisai Ding, Juncheng Li, Jun Wang, Shihui Ying, Jun Shi

The key idea of MEGT is to adopt two independent Efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i. e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM).

Image Classification whole slide images

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction

no code implementations4 May 2023 Qi Wang, Zhijie Wen, Jun Shi, Qian Wang, Dinggang Shen, Shihui Ying

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine.

MRI Reconstruction

Fast MRI Reconstruction via Edge Attention

1 code implementation22 Apr 2023 Hanhui Yang, Juncheng Li, Lok Ming Lui, Shihui Ying, Jun Shi, Tieyong Zeng

To solve this problem, we propose a lightweight and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct images with edge guidance.

MRI Reconstruction

SANDFORMER: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration

no code implementations8 Mar 2023 Jun Shi, Bingcai Wei, Gang Zhou, Liye Zhang

In this paper, we introduce an effective hybrid architecture for sand image restoration tasks, which leverages local features from CNN and long-range dependencies captured by transformer to improve the results further.

Image Restoration

Adverse Weather Removal with Codebook Priors

no code implementations ICCV 2023 Tian Ye, Sixiang Chen, Jinbin Bai, Jun Shi, Chenghao Xue, Jingxia Jiang, Junjie Yin, ErKang Chen, Yun Liu

Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal.


Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR

no code implementations29 Nov 2022 Xiaochuan Ni, Xiaoling Zhang, Xu Zhan, Zhenyu Yang, Jun Shi, Shunjun Wei, Tianjiao Zeng

To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation.

Solving 3D Radar Imaging Inverse Problems with a Multi-cognition Task-oriented Framework

no code implementations28 Nov 2022 Xu Zhan, Xiaoling Zhang, Mou Wang, Jun Shi, Shunjun Wei, Tianjiao Zeng

Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well.

Information Retrieval Retrieval

A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging

no code implementations28 Nov 2022 Yu Ren, Xiaoling Zhang, Xu Zhan, Jun Shi, Shunjun Wei, Tianjiao Zeng

To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.

Compressive Sensing Network Embedding

Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network

no code implementations21 Sep 2022 Xiao Ke, Xiaoling Zhang, Tianwen Zhang, Jun Shi, Shunjun Wei

Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps.

Two Dimensional Sparse-Regularization-Based InSAR Imaging with Back-Projection Embedding

no code implementations21 Sep 2022 Xu Zhan, Xiaoling Zhang, Shunjun Wei, Jun Shi

First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded.

Image Registration Vocal Bursts Valence Prediction

Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution

no code implementations21 Sep 2022 Wensi Zhang, Xiaoling Zhang, Xu Zhan, Yuetonghui Xu, Jun Shi, Shunjun Wei

To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed.

Image Restoration

Constant-Time-Delay Interferences In Near-Field SAR: Analysis And Suppression In Image Domain

no code implementations21 Sep 2022 Xu Zhan, Xiaoling Zhang, Jun Shi, Shunjun Wei

To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work.

Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancement

no code implementations7 Jul 2022 Xiaowo Xu, Xiaoling Zhang, Tianwen Zhang, Zhenyu Yang, Jun Shi, Xu Zhan

Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy.

Shadow Detection

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

1 code implementation27 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.

Contrastive Learning Representation Learning +1

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.

Classification Image Classification

Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images

no code implementations31 May 2022 Jun Shi, Yuanming Zhang, Zheng Li, Xiangmin Han, Saisai Ding, Jun Wang, Shihui Ying

In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models.

Contrastive Learning Federated Learning +1

A channel attention based MLP-Mixer network for motor imagery decoding with EEG

no code implementations21 Oct 2021 Yanbin He, Zhiyang Lu, Jun Wang, Jun Shi

Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task.

EEG Electroencephalogram (EEG)

Causal Incremental Graph Convolution for Recommender System Retraining

1 code implementation16 Aug 2021 Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, Yongdong Zhang

Towards the goal, we propose a \textit{Causal Incremental Graph Convolution} approach, which consists of two new operators named \textit{Incremental Graph Convolution} (IGC) and \textit{Colliding Effect Distillation} (CED) to estimate the output of full graph convolution.

Causal Inference Recommendation Systems

Incremental Learning for Personalized Recommender Systems

no code implementations13 Aug 2021 Yunbo Ouyang, Jun Shi, Haichao Wei, Huiji Gao

Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment.

Incremental Learning Recommendation Systems

Logit Attenuating Weight Normalization

no code implementations12 Aug 2021 Aman Gupta, Rohan Ramanath, Jun Shi, Anika Ramachandran, Sirou Zhou, Mingzhou Zhou, S. Sathiya Keerthi

Over-parameterized deep networks trained using gradient-based optimizers are a popular choice for solving classification and ranking problems.

Image Classification Recommendation Systems

Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

no code implementations29 Jun 2021 Zhiyang Lu, Zheng Li, Jun Wang, Jun Shi, Dinggang Shen

To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training.

Self-Supervised Learning

Task-driven Semantic Coding via Reinforcement Learning

1 code implementation7 Jun 2021 Xin Li, Jun Shi, Zhibo Chen

However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process.

Face Detection License Plate Detection +4

DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images

1 code implementation14 May 2021 Jun Shi, Huite Yi, Shulan Ruan, Zhaohui Wang, Xiaoyu Hao, Hong An, Wei Wei

The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy.

Computed Tomography (CT)

Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions

no code implementations16 Apr 2021 Yushan Zheng, Zhiguo Jiang, Haopeng Zhang, Fengying Xie, Jun Shi, Chenghai Xue

While, it is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database that consists of histopathological whole slide images (WSIs) for a query ROI.

Graph Embedding Image Retrieval +2

SRA-LSTM: Social Relationship Attention LSTM for Human Trajectory Prediction

no code implementations31 Mar 2021 Yusheng Peng, Gaofeng Zhang, Jun Shi, Benzhu Xu, Liping Zheng

Pedestrian trajectory prediction for surveillance video is one of the important research topics in the field of computer vision and a key technology of intelligent surveillance systems.

Pedestrian Trajectory Prediction Trajectory Prediction

Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography

no code implementations15 Jan 2021 Ronglin Gong, Jun Wang, Jun Shi

In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammography-based CAD.

General Classification Image Restoration +2

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.

Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images

no code implementations21 Jul 2020 Tianwen Zhang, Xiaoling Zhang, Jun Shi, Shunjun Wei, Jianguo Wang, Jianwei Li, Hao Su, Yue Zhou

Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1 code implementation6 Apr 2020 Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen

In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

Computed Tomography (CT)

SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration

no code implementations12 Mar 2020 Jun Shi, Jianfeng Xu, Kazuyuki Tasaka, Zhibo Chen

Accelerating the inference speed of CNNs is critical to their deployment in real-world applications.

Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics

no code implementations16 Oct 2019 Jun Shi, Zhibo Chen

Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc.

General Classification Quantization +1

Lightweight Image Super-Resolution with Adaptive Weighted Learning Network

1 code implementation4 Apr 2019 Chaofeng Wang, Zheng Li, Jun Shi

PyTorch code for our paper "Lightweight Image Super-Resolution with Adaptive Weighted Learning Network"

Image Super-Resolution

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