Search Results for author: Jun Cheng

Found 55 papers, 24 papers with code

Image Denoising via Style Disentanglement

no code implementations26 Sep 2023 Jingwei Niu, Jun Cheng, Shan Tan

This leads to the separation of clean contents from noise, effectively denoising the image.

Disentanglement Image Denoising +1

Latent Degradation Representation Constraint for Single Image Deraining

no code implementations9 Sep 2023 Yuhong He, Long Peng, Lu Wang, Jun Cheng

Since rain streaks show a variety of shapes and directions, learning the degradation representation is extremely challenging for single image deraining.

Representation Learning Single Image Deraining

Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising

no code implementations ICCV 2023 Jun Cheng, Tao Liu, Shan Tan

By considering the deep variational image posterior with a Gaussian form, score priors are extracted based on easily accessible minimum MSE Non-$i. i. d$ Gaussian denoisers and variational samples, which in turn facilitate optimizing the variational image posterior.

Image Denoising Variational Inference

ELFNet: Evidential Local-global Fusion for Stereo Matching

1 code implementation ICCV 2023 Jieming Lou, Weide Liu, Zhuo Chen, Fayao Liu, Jun Cheng

Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation.

Domain Generalization Stereo Matching

Empower Your Model with Longer and Better Context Comprehension

1 code implementation25 Jul 2023 YiFei Gao, Lei Wang, Jun Fang, Longhua Hu, Jun Cheng

Recently, with the emergence of numerous Large Language Models (LLMs), the implementation of AI has entered a new era.

MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis

1 code implementation15 Jul 2023 Junyu Li, Han Huang, Dong Ni, Wufeng Xue, Dongmei Zhu, Jun Cheng

In addition, we design an object-level temporal aggregation (OTA) module that can automatically filter low-quality features and efficiently integrate temporal information from multiple frames to improve the accuracy of tumor diagnosis.

Video Classification

Multi-IMU with Online Self-Consistency for Freehand 3D Ultrasound Reconstruction

no code implementations28 Jun 2023 Mingyuan Luo, Xin Yang, Zhongnuo Yan, Junyu Li, Yuanji Zhang, Jiongquan Chen, Xindi Hu, Jikuan Qian, Jun Cheng, Dong Ni

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities.

D3L: Decomposition of 3D Rotation and Lift from 2D Joint to 3D for Human Mesh Recovery

no code implementations10 Jun 2023 Xiaoyang Hao, Han Li, Jun Cheng, Lei Wang

However, these methods present rotation semantic ambiguity, rotation error accumulation, and shape estimation overfitting, which also leads to errors in the estimated pose.

Human Mesh Recovery Pose Estimation

HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder

1 code implementation30 May 2023 Fei Wang, Jun Cheng

To this end, we propose a high-quality decoder (HQDec), with which multilevel near-lossless fine-grained information, obtained by the proposed adaptive axial-normalized position-embedded channel attention sampling module (AdaAxialNPCAS), can be adaptively incorporated into a low-resolution feature map with high-level semantics utilizing the proposed adaptive information exchange scheme.

Monocular Depth Estimation

Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising

1 code implementation25 May 2023 Xuan Liu, Yaoqin Xie, Jun Cheng, Songhui Diao, Shan Tan, Xiaokun Liang

The results demonstrate that our method outperforms the state-of-the-art unsupervised method and surpasses several supervised deep learning-based methods.

Computed Tomography (CT) Image Denoising

No-Reference Point Cloud Quality Assessment via Weighted Patch Quality Prediction

1 code implementation13 May 2023 Jun Cheng, Honglei Su, Jari Korhonen

Then, we gather the features of all the patches of a point cloud for correlation analysis, to obtain the correlation weights.

Point Cloud Quality Assessment

Multi-scale spatial–temporal convolutional neural network for skeleton-based action recognition

1 code implementation journal 2023 Qin Cheng, Jun Cheng, Ziliang Ren, Qieshi Zhang, Jianming Liu

Unifying the MSST module, a multi-scale spatial–temporal convolutional neural network (MSSTNet) is proposed to capture high-level spatial–temporal semantic features for action recognition.

Skeleton Based Action Recognition

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

no code implementations24 Mar 2023 Weide Liu, Zhonghua Wu, Yang Zhao, Yuming Fang, Chuan-Sheng Foo, Jun Cheng, Guosheng Lin

Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes.

Generalized Few-Shot Semantic Segmentation Segmentation +1

Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification

1 code implementation ICCV 2023 Fusheng Hao, Fengxiang He, Liu Liu, Fuxiang Wu, DaCheng Tao, Jun Cheng

This could significantly reduce the efficiency of a large family of few-shot learning algorithms, which have limited data and highly rely on the comparison of image patches.

Few-Shot Image Classification Few-Shot Learning

Spectral Bayesian Uncertainty for Image Super-Resolution

no code implementations CVPR 2023 Tao Liu, Jun Cheng, Shan Tan

In this paper, we propose to quantify spectral Bayesian uncertainty in image SR. To achieve this, a Dual-Domain Learning (DDL) framework is first proposed.

Image Super-Resolution

CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose

no code implementations12 Dec 2022 Fei Wang, Jun Cheng, Penglei Liu

Photometric differences are widely used as supervision signals to train neural networks for estimating depth and camera pose from unlabeled monocular videos.

Model Optimization Self-Supervised Learning

$\textbf{P$^2$A}$: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos

no code implementations26 Jul 2022 Jiang Bian, Qingzhong Wang, Haoyi Xiong, Jun Huang, Chen Liu, Xuhong LI, Jun Cheng, Jun Zhao, Feixiang Lu, Dejing Dou

While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging.

Action Detection Action Localization +2

Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound

no code implementations1 Jul 2022 Han Huang, Yijie Dong, Xiaohong Jia, Jianqiao Zhou, Dong Ni, Jun Cheng, Ruobing Huang

Furthermore, finding an optimal way to integrate multi-view information also relies on the experience of clinicians and adds further difficulty to accurate diagnosis.

Decision Making

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

1 code implementation2 Jun 2022 Fei Wu, Qingzhong Wang, Jian Bian, Haoyi Xiong, Ning Ding, Feixiang Lu, Jun Cheng, Dejing Dou

Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.

Action Recognition Sports Analytics +1

HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images

no code implementations14 Apr 2022 Jikuan Qian, Rui Li, Xin Yang, Yuhao Huang, Mingyuan Luo, Zehui Lin, Wenhui Hong, Ruobing Huang, Haining Fan, Dong Ni, Jun Cheng

The hybrid framework consists of a pre-trained backbone and several searched cells (i. e., network building blocks), which takes advantage of the strengths of both NAS and the expert knowledge from existing convolutional neural networks.

Image Classification Neural Architecture Search

Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis

no code implementations14 Apr 2022 Jiamin Liang, Xin Yang, Yuhao Huang, Haoming Li, Shuangchi He, Xindi Hu, Zejian Chen, Wufeng Xue, Jun Cheng, Dong Ni

Our main contributions include: 1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; 2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN.

Image Generation

Text-to-Image Synthesis Based on Object-Guided Joint-Decoding Transformer

no code implementations CVPR 2022 Fuxiang Wu, Liu Liu, Fusheng Hao, Fengxiang He, Jun Cheng

Object-guided text-to-image synthesis aims to generate images from natural language descriptions built by two-step frameworks, i. e., the model generates the layout and then synthesizes images from the layout and captions.

Image Generation Task 2

Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

no code implementations5 Oct 2021 Kang Zhou, Jing Li, Weixin Luo, Zhengxin Li, Jianlong Yang, Huazhu Fu, Jun Cheng, Jiang Liu, Shenghua Gao

To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.

Anomaly Detection Image Reconstruction

SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

1 code implementation5 Jul 2021 Haocong Rao, Xiping Hu, Jun Cheng, Bin Hu

In this paper, we for the first time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE) framework that comprehensively models human body, component relations, and skeleton dynamics from unlabeled skeleton graphs of various scales to learn an effective skeleton representation for person Re-ID.

Person Re-Identification Relation Network

VEGN: Variant Effect Prediction with Graph Neural Networks

no code implementations25 Jun 2021 Jun Cheng, Carolin Lawrence, Mathias Niepert

In contrast, we propose VEGN, which models variant effect prediction using a graph neural network (GNN) that operates on a heterogeneous graph with genes and variants.

Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

1 code implementation6 Jun 2021 Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu

To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID.

Person Re-Identification

CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation

1 code implementation13 Feb 2021 Shengcong Chen, Changxing Ding, Minfeng Liu, Jun Cheng, DaCheng Tao

Each polygon is represented by a set of centroid-to-boundary distances, which are in turn predicted by features of the centroid pixel for a single nucleus.


CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging

1 code implementation15 Oct 2020 Lei Mou, Yitian Zhao, Huazhu Fu, Yonghuai Liu, Jun Cheng, Yalin Zheng, Pan Su, Jianlong Yang, Li Chen, Alejandro F Frang, Masahiro Akiba, Jiang Liu

Automated detection of curvilinear structures, e. g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases.

Management Segmentation

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification

1 code implementation5 Sep 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Yi Guo, Jun Cheng, Xinwang Liu, Bin Hu

This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.

Contrastive Learning Person Re-Identification +2

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

1 code implementation21 Aug 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, Bin Hu

Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner.

Person Re-Identification

Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

1 code implementation ECCV 2020 Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao

In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.

Anatomy Anomaly Detection +1

Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

2 code implementations1 Aug 2020 Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu

In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised manner.

Action Recognition Contrastive Learning

Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors

no code implementations5 Jun 2020 Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E

It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost.

Computational Physics Materials Science Chemical Physics

RiFeGAN: Rich Feature Generation for Text-to-Image Synthesis From Prior Knowledge

no code implementations CVPR 2020 Jun Cheng, Fuxiang Wu, Yanling Tian, Lei Wang, Dapeng Tao

Text-to-image synthesis is a challenging task that generates realistic images from a textual sequence, which usually contains limited information compared with the corresponding image and so is ambiguous and abstractive.

Image Generation

Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image

no code implementations28 Nov 2019 Kang Zhou, Shenghua Gao, Jun Cheng, Zaiwang Gu, Huazhu Fu, Zhi Tu, Jianlong Yang, Yitian Zhao, Jiang Liu

With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images.

Anomaly Detection

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

3 code implementations7 Mar 2019 Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, Jiang Liu

In this paper, we propose a context encoder network (referred to as CE-Net) to capture more high-level information and preserve spatial information for 2D medical image segmentation.

Cell Segmentation Image Segmentation +4

Towards Query Efficient Black-box Attacks: An Input-free Perspective

1 code implementation9 Sep 2018 Yali Du, Meng Fang, Jin-Feng Yi, Jun Cheng, DaCheng Tao

First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model.

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading

no code implementations31 Aug 2018 Kang Zhou, Zaiwang Gu, Wen Liu, Weixin Luo, Jun Cheng, Shenghua Gao, Jiang Liu

To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression.

Diabetic Retinopathy Grading General Classification +2

Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image

3 code implementations19 May 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Changqing Zhang, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.

Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks

no code implementations22 Apr 2018 Fusheng Hao, Jun Cheng, Lei Wang, Xinchao Wang, Jianzhong Cao, Xiping Hu, Dapeng Tao

Discriminative features are obtained by constraining the deep CNNs to map training samples to the corresponding anchors as close as possible.

Image Classification Test

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

3 code implementations3 Jan 2018 Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu, Xiaochun Cao

The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function.


Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision

no code implementations12 Nov 2016 Dapeng Luo, Zhipeng Zeng, Nong Sang, Xiang Wu, Longsheng Wei, Quanzheng Mou, Jun Cheng, Chen Luo

In this paper, the proposed framework takes a remarkably different direction to resolve the multi-scene detection problem in a bottom-up fashion.

object-detection Object Detection +1

Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition

no code implementations7 Aug 2016 Yanan Guo, Lei LI, Weifeng Liu, Jun Cheng, Dapeng Tao

Since human actions can be characterized by multiple feature representations extracted from Kinect and inertial sensors, multiview features must be encoded into a unified space optimal for human action recognition.

Action Recognition Temporal Action Localization

Multiview Hessian Discriminative Sparse Coding for Image Annotation

no code implementations15 Jul 2013 Weifeng Liu, DaCheng Tao, Jun Cheng, Yuanyan Tang

In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation.

Image Denoising Image Inpainting +1

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