Search Results for author: Yinghuan Shi

Found 34 papers, 12 papers with code

Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation

1 code implementation17 Oct 2021 Yinghuan Shi, Jian Zhang, Tong Ling, Jiwen Lu, Yefeng Zheng, Qian Yu, Lei Qi, Yang Gao

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.

Medical Image Segmentation

Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization

no code implementations10 Oct 2021 Ruiqi Wang, Lei Qi, Yinghuan Shi, Yang Gao

With the goal of directly generalizing trained models to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention.

Domain Generalization

LibFewShot: A Comprehensive Library for Few-shot Learning

1 code implementation10 Sep 2021 Wenbin Li, Chuanqi Dong, Pinzhuo Tian, Tiexin Qin, Xuesong Yang, Ziyi Wang, Jing Huo, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo

Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmark datasets with multiple backbone architectures to evaluate common pitfalls and effects of different training tricks.

Data Augmentation Few-Shot Image Classification +1

Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

1 code implementation24 Jul 2021 Qian Yu, Lei Qi, Luping Zhou, Lei Wang, Yilong Yin, Yinghuan Shi, Wuzhang Wang, Yang Gao

Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net.

Medical Image Segmentation

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

1 code implementation9 Jun 2021 Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao

In this paper, we investigate if we could make the self-training -- a simple but popular framework -- work better for semi-supervised segmentation.

Semi-Supervised Semantic Segmentation

Feature-based Style Randomization for Domain Generalization

no code implementations6 Jun 2021 Yue Wang, Lei Qi, Yinghuan Shi, Yang Gao

As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target domain without any additional adaption.

Data Augmentation Domain Generalization

Mining Latent Classes for Few-shot Segmentation

1 code implementation ICCV 2021 Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao

Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.

Rectification

Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation

no code implementations18 Jan 2021 Xiaoting Han, Lei Qi, Qian Yu, Ziqi Zhou, Yefeng Zheng, Yinghuan Shi, Yang Gao

These typical methods usually utilize a translation network to transform images from the source domain to target domain or train the pixel-level classifier merely using translated source images and original target images.

Medical Image Segmentation Translation +1

Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective

no code implementations31 Jul 2020 Yinghuan Shi, Wanqi Yang, Kim-Han Thung, Hao Wang, Yang Gao, Yang Pan, Li Zhang, Dinggang Shen

Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.

Manifold Alignment for Semantically Aligned Style Transfer

1 code implementation ICCV 2021 Jing Huo, Shiyin Jin, Wenbin Li, Jing Wu, Yu-Kun Lai, Yinghuan Shi, Yang Gao

In this paper, we make a new assumption that image features from the same semantic region form a manifold and an image with multiple semantic regions follows a multi-manifold distribution.

Semantic Segmentation Style Transfer

Class Distribution Alignment for Adversarial Domain Adaptation

no code implementations20 Apr 2020 Wanqi Yang, Tong Ling, Chengmei Yang, Lei Wang, Yinghuan Shi, Luping Zhou, Ming Yang

To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains.

General Classification Translation +1

Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

1 code implementation13 Apr 2020 Tiexin Qin, Wenbin Li, Yinghuan Shi, Yang Gao

Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model learn more robust feature representations.

Data Augmentation Few-Shot Learning

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)

Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation

no code implementations3 Apr 2020 Qian Yu, Yinghuan Shi, Yefeng Zheng, Yang Gao, Jianbing Zhu, Yakang Dai

Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients.

Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

no code implementations22 Feb 2020 Tiexin Qin, Ziyuan Wang, Kelei He, Yinghuan Shi, Yang Gao, Dinggang Shen

Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance for medical image segmentation.

Data Augmentation Medical Image Segmentation +1

Asymmetric Distribution Measure for Few-shot Learning

no code implementations1 Feb 2020 Wenbin Li, Lei Wang, Jing Huo, Yinghuan Shi, Yang Gao, Jiebo Luo

Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning.

Few-Shot Image Classification

Differentiable Meta-learning Model for Few-shot Semantic Segmentation

no code implementations23 Nov 2019 Pinzhuo Tian, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, Yang Gao

To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm.

Few-Shot Semantic Segmentation Semantic Segmentation

Automatic Data Augmentation by Learning the Deterministic Policy

1 code implementation18 Oct 2019 Yinghuan Shi, Tiexin Qin, Yong liu, Jiwen Lu, Yang Gao, Dinggang Shen

By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.

Data Augmentation Q-Learning

Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification

no code implementations15 Aug 2019 Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi, Yang Gao

In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels.

Semi-Supervised Person Re-Identification

GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification

no code implementations14 Aug 2019 Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi, Yang Gao

Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch.

Person Re-Identification

Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification

no code implementations2 Aug 2019 Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi, Xin Geng, Yang Gao

To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.

Person Re-Identification

A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification

no code implementations ICCV 2019 Lei Qi, Lei Wang, Jing Huo, Luping Zhou, Yinghuan Shi, Yang Gao

For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains.

Person Re-Identification Representation Learning +1

Online Progressive Deep Metric Learning

1 code implementation15 May 2018 Wenbin Li, Jing Huo, Yinghuan Shi, Yang Gao, Lei Wang, Jiebo Luo

Furthermore, in a progressively and nonlinearly learning way, ODML has a stronger learning ability than traditional shallow online metric learning in the case of limited available training data.

Metric Learning

Crossbar-Net: A Novel Convolutional Network for Kidney Tumor Segmentation in CT Images

no code implementations27 Apr 2018 Qian Yu, Yinghuan Shi, Jinquan Sun, Yang Gao, Yakang Dai, Jianbing Zhu

Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task.

Cardiac Segmentation Tumor Segmentation

MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification

no code implementations11 Apr 2018 Lei Qi, Jing Huo, Lei Wang, Yinghuan Shi, Yang Gao

Lastly, considering person retrieval is a special image retrieval task, we propose a novel ranking loss to optimize the whole network.

Image Retrieval Person Re-Identification +1

The Automatic Identification of Butterfly Species

no code implementations18 Mar 2018 Juanying Xie, Qi Hou, Yinghuan Shi, Lv Peng, Liping Jing, Fuzhen Zhuang, Junping Zhang, Xiaoyang Tang, Shengquan Xu

We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment.

Revisiting Metric Learning for SPD Matrix Based Visual Representation

no code implementations CVPR 2017 Luping Zhou, Lei Wang, Jianjia Zhang, Yinghuan Shi, Yang Gao

The proposed method has been tested on multiple SPD-based visual representation data sets used in the literature, and the results demonstrate its interesting properties and attractive performance.

Metric Learning

WebCaricature: a benchmark for caricature recognition

no code implementations9 Mar 2017 Jing Huo, Wenbin Li, Yinghuan Shi, Yang Gao, Hujun Yin

In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition.

Caricature Face Recognition

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

no code implementations29 Sep 2016 Wenbin Li, Yang Gao, Lei Wang, Luping Zhou, Jing Huo, Yinghuan Shi

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper.

Event Detection Face Verification +1

Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis

no code implementations CVPR 2014 Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen

Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways.

Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso

no code implementations CVPR 2013 Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen

Specifically, to segment the prostate in the current treatment image, the physician first takes a few seconds to manually specify the first and last slices of the prostate in the image space.

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