no code implementations • 11 Apr 2024 • Zeyu Zhang, Yuanshen Zhao, Jingxian Duan, Yaou Liu, Hairong Zheng, Dong Liang, Zhenyu Zhang, Zhi-Cheng Li
The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph.
no code implementations • 6 Apr 2024 • Dong Liang, Zhengyan Xu, Ling Li, Mingqiang Wei, Songcan Chen
In this paper, we propose a physics-inspired contrastive learning paradigm for low-light enhancement, called PIE.
no code implementations • 22 Mar 2024 • Jun Cheng, Dong Liang, Shan Tan
Image denoising is a fundamental task in computer vision.
no code implementations • 24 Nov 2023 • Taofeng Xie, Zhuo-Xu Cui, Chen Luo, Huayu Wang, Congcong Liu, Yuanzhi Zhang, Xuemei Wang, Yanjie Zhu, Qiyu Jin, Guoqing Chen, Yihang Zhou, Dong Liang, Haifeng Wang
The complementary information can contribute to image reconstruction.
no code implementations • 16 Nov 2023 • Wei zhang, Dai Li, Chen Liang, Fang Zhou, Zhongke Zhang, Xuewei Wang, Ru Li, Yi Zhou, Yaning Huang, Dong Liang, Kai Wang, Zhangyuan Wang, Zhengxing Chen, Min Li, Fenggang Wu, Minghai Chen, Huayu Li, Yunnan Wu, Zhan Shu, Mindi Yuan, Sri Reddy
To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models.
1 code implementation • 6 Nov 2023 • Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu
However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution.
no code implementations • 26 Sep 2023 • Fan Li, Dong Liang, Jing Lian, Qidong Liu, Hegui Zhu, Jizhao Liu
Most current gait recognition methods suffer from poor interpretability and high computational cost.
no code implementations • 24 Sep 2023 • Chen Luo, Huayu Wang, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, Dong Liang
However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications.
no code implementations • 17 Sep 2023 • Huayu Wang, Chen Luo, Taofeng Xie, Qiyu Jin, Guoqing Chen, Zhuo-Xu Cui, Dong Liang
We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold.
1 code implementation • 2 Sep 2023 • Yu Guan, Chuanming Yu, Shiyu Lu, Zhuoxu Cui, Dong Liang, Qiegen Liu
In this study, leveraging a combination of the properties of k-space data and the diffusion process, our novel scheme focuses on mining the multi-frequency prior with different strategies to pre-serve fine texture details in the reconstructed image.
1 code implementation • 31 Aug 2023 • Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li
In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.
no code implementations • 30 Aug 2023 • Zhuo-Xu Cui, Congcong Liu, Xiaohong Fan, Chentao Cao, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Yihang Zhou, Haifeng Wang, Yanjie Zhu, Jianping Zhang, Qiegen Liu, Dong Liang
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
no code implementations • 5 Aug 2023 • Fanshi Li, Zhihui Wang, Yifan Guo, Congcong Liu, Yanjie Zhu, Yihang Zhou, Jun Li, Dong Liang, Haifeng Wang
In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer's disease (AD) diagnosis performance.
1 code implementation • 30 Jul 2023 • Dong Liang, Xingyu Qiu, Kuanquan Wang, Gongning Luo, Wei Wang, Yashu Liu
Many retrospective algorithms were developed to facilitate the bias correction, to which the deep learning-based methods outperformed.
no code implementations • 19 Jun 2023 • Jiandong Su, Kun Shang, Dong Liang
In this work, we incorporate the artifact generation mechanism to reestablish the relationship between artifacts and anatomical content in the image domain, highlighting the superiority of explicit models over implicit models in medical problems.
no code implementations • 18 Jun 2023 • Jiandong Su, Ce Wang, Yinsheng Li, Kun Shang, Dong Liang
Metal artifacts is a major challenge in computed tomography (CT) imaging, significantly degrading image quality and making accurate diagnosis difficult.
no code implementations • 8 Jun 2023 • Gongshu Wang, Ning Jiang, Yunxiao Ma, Tiantian Liu, Duanduan Chen, Jinglong Wu, Guoqi Li, Dong Liang, Tianyi Yan
In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis.
no code implementations • 25 May 2023 • Dong Liang, Martin Guay, Shimin Wang
In this paper, a bipartite output regulation problem is solved for a class of nonlinear multi-agent systems subject to static signed communication networks.
no code implementations • 14 May 2023 • Xuebao Cai, Yuhang Tan, Ting Su, Dong Liang, Hairong Zheng, Jinyou Xu, Peiping Zhu, Yongshuai Ge
In conclusion, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nano-resolution phase contrast imaging.
no code implementations • 6 May 2023 • Taofeng Xie, Chentao Cao, Zhuoxu Cui, Yu Guo, Caiying Wu, Xuemei Wang, Qingneng Li, Zhanli Hu, Tao Sun, Ziru Sang, Yihang Zhou, Yanjie Zhu, Dong Liang, Qiyu Jin, Guoqing Chen, Haifeng Wang
JPD of MRI and noise-added PET was learned in the diffusion process.
no code implementations • 4 May 2023 • Zhuo-Xu Cui, Congcong Liu, Chentao Cao, Yuanyuan Liu, Jing Cheng, Qingyong Zhu, Yanjie Zhu, Haifeng Wang, Dong Liang
We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.
no code implementations • 28 Apr 2023 • Ling Li, Dong Liang, Yuanhang Gao, Sheng-Jun Huang, Songcan Chen
In this paper, we propose a new paradigm, i. e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward.
no code implementations • 11 Apr 2023 • Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method.
no code implementations • 24 Mar 2023 • Mingqiang Wei, Qian Sun, Haoran Xie, Dong Liang, Fu Lee Wang
Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.
1 code implementation • ICCV 2023 • Yuyan Zhou, Dong Liang, Songcan Chen, Sheng-Jun Huang, Shuo Yang, Chongyi Li
In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy.
no code implementations • 15 Dec 2022 • Chuanming Yu, Yu Guan, Ziwen Ke, Dong Liang, Qiegen Liu
Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements.
no code implementations • 14 Dec 2022 • Chentao Cao, Zhuo-Xu Cui, Jing Cheng, Sen Jia, Hairong Zheng, Dong Liang, Yanjie Zhu
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction.
1 code implementation • 6 Dec 2022 • Dong Liang, Jing-Wei Zhang, Ying-Peng Tang, Sheng-Jun Huang
However, existing active learning methods are mainly with class-balanced settings and image-based querying for generic object detection tasks, which are less applicable to aerial object detection scenarios due to the long-tailed class distribution and dense small objects in aerial scenes.
no code implementations • 24 Nov 2022 • Zhuo-Xu Cui, Qingyong Zhu, Jing Cheng, Dong Liang
Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems.
no code implementations • 4 Nov 2022 • Zhengyong Huang, Sijuan Zou, Guoshuai Wang, Zixiang Chen, Hao Shen, HaiYan Wang, Na Zhang, Lu Zhang, Fan Yang, Haining Wangg, Dong Liang, Tianye Niu, Xiaohua Zhuc, Zhanli Hua
In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information.
no code implementations • 3 Nov 2022 • Ce Wang, Kun Shang, Haimiao Zhang, Shang Zhao, Dong Liang, S. Kevin Zhou
Experiments on the VerSe dataset demonstrate this ability of our sampling policy, which is difficult to achieve based on uniform sampling.
no code implementations • 28 Oct 2022 • Huajian Si, Zeyong Wei, Zhe Zhu, Honghua Chen, Dong Liang, Weiming Wang, Mingqiang Wei
Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising.
no code implementations • 2 Sep 2022 • Zhuo-Xu Cui, Chentao Cao, Shaonan Liu, Qingyong Zhu, Jing Cheng, Haifeng Wang, Yanjie Zhu, Dong Liang
Recently, score-based diffusion models have shown satisfactory performance in MRI reconstruction.
2 code implementations • 15 Aug 2022 • Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.
1 code implementation • 11 Aug 2022 • Zhuo-Xu Cui, Sen Jia, Qingyong Zhu, Congcong Liu, Zhilang Qiu, Yuanyuan Liu, Jing Cheng, Haifeng Wang, Yanjie Zhu, Dong Liang
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.
1 code implementation • 10 Aug 2022 • Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, Yanjie Zhu
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation.
no code implementations • 17 Jul 2022 • Yuanyuan Liu, Dong Liang, Zhuo-Xu Cui, Yuxin Yang, Chentao Cao, Qingyong Zhu, Jing Cheng, Caiyun Shi, Haifeng Wang, Yanjie Zhu
Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR T1\r{ho} imaging.
1 code implementation • 21 Jun 2022 • Dong Liang, Jun Liu, Kuanquan Wang, Gongning Luo, Wei Wang, Shuo Li
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.
no code implementations • 9 May 2022 • Chentao Cao, Zhuo-Xu Cui, Qingyong Zhu, Congcong Liu, Dong Liang, Yanjie Zhu
In this paper, we propose a learned low-rank method for dynamic MR imaging.
1 code implementation • 8 May 2022 • Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Pengwen Zhu, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu
Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).
1 code implementation • 5 Apr 2022 • Hui Tao, Haifeng Wang, Shanshan Wang, Dong Liang, Xiaoling Xu, Qiegen Liu
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology.
1 code implementation • 21 Mar 2022 • Zongjiang Tu, Chen Jiang, Yu Guan, Shanshan Wang, Jijun Liu, Qiegen Liu, Dong Liang
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.
1 code implementation • 19 Jan 2022 • Xianghao Liao, Shanshan Wang, Lanlan Tu, Yuhao Wang, Dong Liang, Qiegen Liu
Additionally, its performance is not susceptible to different number of virtual coils.
no code implementations • 18 Dec 2021 • Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Kankan Zhao, Ziwen Ke, Wenqi Huang, Haifeng Wang, Yanjie Zhu, Dong Liang
Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model.
1 code implementation • 13 Dec 2021 • Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, Huiyu Zhou
Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images.
1 code implementation • 13 Dec 2021 • Dong Liang, Qixiang Geng, Zongqi Wei, Dmitry A. Vorontsov, Ekaterina L. Kim, Mingqiang Wei, Huiyu Zhou
On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.
Ranked #15 on Object Detection In Aerial Images on DOTA (using extra training data)
no code implementations • 1 Dec 2021 • Minghan Fu, Yanhua Duan, Zhaoping Cheng, Wenjian Qin, Ying Wang, Dong Liang, Zhanli Hu
The derived architecture is referred to as the Teacher-Student Consistency Network (TSC-Net), which consists of the teacher network and the student network with identical architecture.
no code implementations • 1 Dec 2021 • Yanjie Zhu, Haoxiang Li, Yuanyuan Liu, Muzi Guo, Guanxun Cheng, Gang Yang, Haifeng Wang, Dong Liang
Methods: The proposed framework consists of a reconstruction module and a generative module.
1 code implementation • 7 Sep 2021 • Yu Guan, Zongjiang Tu, Shanshan Wang, Qiegen Liu, Yuhao Wang, Dong Liang
In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image.
1 code implementation • 8 Aug 2021 • Han Sun, Jun Cen, Ningzhong Liu, Dong Liang, Huiyu Zhou
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection.
no code implementations • 20 Jun 2021 • Xu Li, Wenyao Zhai, Morris Repeta, Hua Cai, Tyler Ross, Kimia Ansari, Sam Tiller, Hari Krishna Pothula, Dong Liang, Fan Yang, Yibo Lyu, Songlin Shuai, Guangjian Wang, Wen Tong
For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.
no code implementations • 13 Jun 2021 • Chenxin Li, Qi Qi, Xinghao Ding, Yue Huang, Dong Liang, Yizhou Yu
In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification.
1 code implementation • 8 May 2021 • Dong Liang, Fei Xue, Ling Li
Concealed object detection in Terahertz imaging is an urgent need for public security and counter-terrorism.
no code implementations • 13 Apr 2021 • Wenqi Huang, Sen Jia, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Yanjie Zhu, Dong Liang
Improving the image resolution and acquisition speed of magnetic resonance imaging (MRI) is a challenging problem.
1 code implementation • 21 Mar 2021 • Zongqi Wei, Dong Liang, Dong Zhang, Liyan Zhang, Qixiang Geng, Mingqiang Wei, Huiyu Zhou
Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance.
1 code implementation • 9 Mar 2021 • Ziwen Ke, Zhuo-Xu Cui, Wenqi Huang, Jing Cheng, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
1 code implementation • 26 Oct 2020 • Wenqi Huang, Ziwen Ke, Zhuo-Xu Cui, Jing Cheng, Zhilang Qiu, Sen Jia, Leslie Ying, Yanjie Zhu, Dong Liang
However, the selection of the parameters of L+S is empirical, and the acceleration rate is limited, which are common failings of iterative compressed sensing MR imaging (CS-MRI) reconstruction methods.
no code implementations • 9 Sep 2020 • Wei Wang, Yanjie Zhu, Zhuoxu Cui, Dong Liang
Convolutional neural network (CNN) models have achieved great success in many fields.
5 code implementations • 14 Aug 2020 • Cong Quan, Jinjie Zhou, Yuanzheng Zhu, Yang Chen, Shan-Shan Wang, Dong Liang, Qiegen Liu
Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.
no code implementations • 8 Aug 2020 • Wei Wang, Lin Cheng, Yanjie Zhu, Dong Liang
In recent times, using small data to train networks has become a hot topic in the field of deep learning.
no code implementations • 22 Jun 2020 • Ziwen Ke, Wenqi Huang, Jing Cheng, Zhuoxu Cui, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
no code implementations • 21 May 2020 • Yiyang Shen, Yidan Feng, Sen Deng, Dong Liang, Jing Qin, Haoran Xie, Mingqiang Wei
We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.
no code implementations • 29 Mar 2020 • Wenjun Zhou, Yuheng Deng, Bo Peng, Dong Liang, Shun'ichi Kaneko
Background initialization is an important step in many high-level applications of video processing, ranging from video surveillance to video inpainting. However, this process is often affected by practical challenges such as illumination changes, background motion, camera jitter and intermittent movement, etc. In this paper, we develop a co-occurrence background model with superpixel segmentation for robust background initialization.
no code implementations • 3 Feb 2020 • Hongyu Li, Zifei Liang, Chaoyi Zhang, Ruiying Liu, Jing Li, Weihong Zhang, Dong Liang, Bowen Shen, Xiaoliang Zhang, Yulin Ge, Jiangyang Zhang, Leslie Ying
We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.
1 code implementation • 20 Dec 2019 • Ziwen Ke, Jing Cheng, Leslie Ying, Hairong Zheng, Yanjie Zhu, Dong Liang
Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied.
1 code implementation • 23 Oct 2019 • Zhuonan He, Jinjie Zhou, Dong Liang, Yuhao Wang, Qiegen Liu
Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging.
2 code implementations • 24 Sep 2019 • Yiling Liu, Qiegen Liu, Minghui Zhang, Qingxin Yang, Shan-Shan Wang, Dong Liang
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.
1 code implementation • 3 Sep 2019 • Siyuan Wang, Junjie Lv, Yuanyuan Hu, Dong Liang, Minghui Zhang, Qiegen Liu
At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).
no code implementations • 7 Aug 2019 • Jing Cheng, Haifeng Wang, Leslie Ying, Dong Liang
Experi-ments on in vivo MR data demonstrate that the proposed method achieves supe-rior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.
no code implementations • 26 Jul 2019 • Dong Liang, Jing Cheng, Ziwen Ke, Leslie Ying
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI.
no code implementations • 19 Jun 2019 • Jing Cheng, Haifeng Wang, Yanjie Zhu, Qiegen Liu, Qiyang Zhang, Ting Su, Jianwei Chen, Yongshuai Ge, Zhanli Hu, Xin Liu, Hairong Zheng, Leslie Ying, Dong Liang
Usually, acquiring less data is a direct but important strategy to address these issues.
1 code implementation • 11 Jun 2019 • Shan-Shan Wang, Huitao Cheng, Leslie Ying, Taohui Xiao, Ziwen Ke, Xin Liu, Hairong Zheng, Dong Liang
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.
no code implementations • 18 Jan 2019 • Ziwen Ke, Shan-Shan Wang, Huitao Cheng, Leslie Ying, Qiegen Liu, Hairong Zheng, Dong Liang
In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN.
1 code implementation • 25 Nov 2018 • Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou
Human image generation is a very challenging task since it is affected by many factors.
no code implementations • EMNLP 2018 • Jingjing Gong, Xipeng Qiu, Xinchi Chen, Dong Liang, Xuanjing Huang
Attention-based neural models have achieved great success in natural language inference (NLI).
no code implementations • 30 Sep 2018 • Shan-Shan Wang, Ziwen Ke, Huitao Cheng, Sen Jia, Ying Leslie, Hairong Zheng, Dong Liang
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time.
no code implementations • 9 Sep 2018 • Houtao Deng, Ganesh Krishnan, Ji Chen, Dong Liang
Demand variance can result in a mismatch between planned supply and actual demand.
no code implementations • ICCV 2017 • Qiong Luo, Zhi Han, Xi'ai Chen, Yao Wang, Deyu Meng, Dong Liang, Yandong Tang
In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG).
no code implementations • 27 Jun 2016 • Shi Yue, Zhong Erheng, Rajan Suju, Dong Liang, Tseng Hao-wei, Li Beitao
Blog recommendation is challenging since most mobile users would suffer from the cold start when there are only a limited number of blogs followed by the user.
no code implementations • 31 Aug 2015 • Wang James Z., Zhang Yuanyuan, Dong Liang, Li Lin, Srimani Pradip K, Yu Philip S.
To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: parallel document index creation, ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database.
no code implementations • 24 May 2014 • Dong Liang, Shun'ichi Kaneko
Change detection plays an important role in most video-based applications.