Search Results for author: Jian-Guo Zhang

Found 26 papers, 9 papers with code

HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization

no code implementations12 Oct 2021 Ye Liu, Jian-Guo Zhang, Yao Wan, Congying Xia, Lifang He, Philip S. Yu

To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model.

Document Summarization Extractive Summarization +1

Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation

no code implementations22 Jan 2021 Ye Liu, Yao Wan, Jian-Guo Zhang, Wenting Zhao, Philip S. Yu

In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance.

Machine Translation Translation

Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation

no code implementations3 Aug 2020 Hongwei Li, Jian-Guo Zhang, Bjoern Menze

Tackling domain shifts in multi-centre and multi-vendor data sets remains challenging for cardiac image segmentation.

Data Augmentation Semantic Segmentation

Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns

no code implementations25 Jan 2020 Hongwei Li, Timo Loehr, Anjany Sekuboyina, Jian-Guo Zhang, Benedikt Wiestler, Bjoern Menze

In medical imaging, the heterogeneity of multi-centre data impedes the applicability of deep learning-based methods and results in significant performance degradation when applying models in an unseen data domain, e. g. a new centreor a new scanner.

Lesion Segmentation Unsupervised Domain Adaptation

DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis

1 code implementation29 Apr 2019 Hongwei Li, Johannes C. Paetzold, Anjany Sekuboyina, Florian Kofler, Jian-Guo Zhang, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze

Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).

Image Generation

Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce

no code implementations NAACL 2019 Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Xiuming Pan, Yu Gong, Philip S. Yu

To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process.

Product Title Refinement via Multi-Modal Generative Adversarial Learning

no code implementations11 Nov 2018 Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Ye Liu, Xiuming Pan, Yu Gong, Philip S. Yu

Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

no code implementations11 Sep 2018 Jian-Guo Zhang, Ji Wang, Lifang He, Zhao Li, Philip S. Yu

Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.

Anomaly Detection

Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

no code implementations10 Sep 2018 Ji Wang, Jian-Guo Zhang, Weidong Bao, Xiaomin Zhu, Bokai Cao, Philip S. Yu

To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.

Early action prediction by soft regression

no code implementations IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 Jian-Fang Hu, Wei-Shi Zheng, Lianyang Ma, Gang Wang, Jian-Huang Lai, Jian-Guo Zhang

Our formulation of soft regression framework 1) overcomes a usual assumption in existing early action prediction systems that the progress level of on-going sequence is given in the testing stage; and 2) presents a theoretical framework to better resolve the ambiguity and uncertainty of subsequences at early performing stage.

Skeleton Based Action Recognition

Multi-Scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities Segmentation

no code implementations13 Jul 2018 Hongwei Li, Jian-Guo Zhang, Mark Muehlau, Jan Kirschke, Bjoern Menze

Secondly, we aggregate multi-scale Stack-Nets with different receptive fields to learn multi-scale contextual information of both large and small lesions.

Lesion Segmentation

Discriminative Learning of Latent Features for Zero-Shot Recognition

1 code implementation CVPR 2018 Yan Li, Junge Zhang, Jian-Guo Zhang, Kaiqi Huang

In this work, we retrospect existing methods and demonstrate the necessity to learn discriminative representations for both visual and semantic instances of ZSL.

Zero-Shot Learning

Mixed Supervised Object Detection with Robust Objectness Transfer

no code implementations27 Feb 2018 Yan Li, Junge Zhang, Kaiqi Huang, Jian-Guo Zhang

Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD.

Multiple Instance Learning Object Detection

Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images

2 code implementations14 Feb 2018 Hongwei Li, Gongfa Jiang, Jian-Guo Zhang, Ruixuan Wang, Zhaolei Wang, Wei-Shi Zheng, Bjoern Menze

In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans.

Data Augmentation

Beyond triplet loss: a deep quadruplet network for person re-identification

no code implementations CVPR 2017 Weihua Chen, Xiaotang Chen, Jian-Guo Zhang, Kaiqi Huang

In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID.

Person Re-Identification

Jointly learning heterogeneous features for rgb-d activity recognition

no code implementations IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 39 , Issue: 11 , Nov. 1 2017 ) 2016 Jian-Fang Hu, Wei-Shi Zheng, Jian-Huang Lai, Jian-Guo Zhang

The proposed model formed in a unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to exploit latent shared features across different feature channels, 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces, and 3) transferring feature-specific intermediate transforms (i-transforms) for learning fusion of heterogeneous features across datasets.

Activity Recognition Human-Object Interaction Detection +2

A Multi-task Deep Network for Person Re-identification

no code implementations19 Jul 2016 Weihua Chen, Xiaotang Chen, Jian-Guo Zhang, Kaiqi Huang

Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches.

Person Re-Identification

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