no code implementations • ECCV 2020 • Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, Meng Wang
It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results.
no code implementations • ECCV 2020 • Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng
In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.
no code implementations • ECCV 2020 • Xiaobo Wang, Tianyu Fu, Shengcai Liao, Shuo Wang, Zhen Lei, Tao Mei
Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.
no code implementations • 23 Dec 2022 • Haoran Wang, Yan Zhu, Wenzheng Qin, Yizhe Zhang, Pinghong Zhou, QuanLin Li, Shuo Wang, Zhijian Song
In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis.
no code implementations • 10 Dec 2022 • Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, Luc van Gool
In this work, we adapt such depth inference models for object segmentation using the objects' ``pop-out'' prior in 3D.
no code implementations • 24 Nov 2022 • Seonhye Park, Alsharif Abuadbba, Shuo Wang, Kristen Moore, Yansong Gao, Hyoungshick Kim, Surya Nepal
In this work, we propose a novel DNN fingerprinting technique dubbed DEEPTASTER to prevent a new attack scenario in which a victim's data is stolen to build a suspect model.
no code implementations • 17 Nov 2022 • Jiahao Chang, Shuo Wang, Guangkai Xu, Zehui Chen, Chenhongyi Yang, Feng Zhao
Transformer-based detectors (DETRs) have attracted great attention due to their sparse training paradigm and the removal of post-processing operations, but the huge model can be computationally time-consuming and difficult to be deployed in real-world applications.
no code implementations • 11 Nov 2022 • Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Amit R. Patel, Miaomiao Zhang
Automated identification of myocardial scar from late gadolinium enhancement cardiac magnetic resonance images (LGE-CMR) is limited by image noise and artifacts such as those related to motion and partial volume effect.
no code implementations • 11 Nov 2022 • Jiarui Xing, Shuo Wang, Kenneth C. Bilchick, Frederick H. Epstein, Amit R. Patel, Miaomiao Zhang
With a newly introduced auxiliary LMA region classification sub-network, our proposed model shows more robustness to the complex pattern cause by myocardial scar, significantly eliminates their negative effects in LMA detection, and in turn improves the performance of scar classification.
no code implementations • 12 Oct 2022 • Shuo Wang, Chen Qin, Chengyan Wang, Kang Wang, Haoran Wang, Chen Chen, Cheng Ouyang, Xutong Kuang, Chengliang Dai, Yuanhan Mo, Zhang Shi, Chenchen Dai, Xinrong Chen, He Wang, Wenjia Bai
The quality of cardiac magnetic resonance (CMR) imaging is susceptible to respiratory motion artifacts.
1 code implementation • 28 Aug 2022 • Mengyun Qiao, Berke Doga Basaran, Huaqi Qiu, Shuo Wang, Yi Guo, Yuanyuan Wang, Paul M. Matthews, Daniel Rueckert, Wenjia Bai
Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.
no code implementations • 4 Aug 2022 • Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz, Daniel Rueckert
In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable.
1 code implementation • 15 Jul 2022 • Zhicai Wang, Yanbin Hao, Xingyu Gao, Hao Zhang, Shuo Wang, Tingting Mu, Xiangnan He
They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.
1 code implementation • 4 Jul 2022 • Chang Liu, Gang Yang, Shuo Wang, Hangxu Wang, Yunhua Zhang, Yutao Wang
We employ the powerful feature extraction capability of Transformer (PVTv2) to extract global semantic information from RGB data and design a lightweight CNN backbone (LWDepthNet) to extract spatial structure information from depth data without pre-training.
no code implementations • 3 Jul 2022 • Lin Li, Jingyi Liu, Shuo Wang, Xunkun Wang, Tian-Zhu Xiang
Trichomoniasis is a common infectious disease with high incidence caused by the parasite Trichomonas vaginalis, increasing the risk of getting HIV in humans if left untreated.
1 code implementation • 2 Jul 2022 • Yujia Sun, Shuo Wang, Chenglizhao Chen, Tian-Zhu Xiang
Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task.
1 code implementation • 8 Jun 2022 • Chen Qin, Shuo Wang, Chen Chen, Wenjia Bai, Daniel Rueckert
In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model.
no code implementations • 2 Jun 2022 • Chengliang Dai, Shuo Wang, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation.
1 code implementation • 23 May 2022 • Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu
In this work, we propose a template-based method that can yield results with high translation quality and match accuracy and the inference speed of our method is comparable with unconstrained NMT models.
no code implementations • 20 May 2022 • Wenxuan Wang, Wenxiang Jiao, Shuo Wang, Zhaopeng Tu, Michael R. Lyu
Zero-shot translation is a promising direction for building a comprehensive multilingual neural machine translation (MNMT) system.
no code implementations • 11 May 2022 • Xiaoqin Zhang, Ziwei Huang, Jingjing Zheng, Shuo Wang, Xianta Jiang
The task of grasp pattern recognition aims to derive the applicable grasp types of an object according to the visual information.
no code implementations • 5 May 2022 • Weichen Fan, Yuanbo Yang, Kunpeng Qiu, Shuo Wang, Yongxin Guo
Therefore, to address the generalization problem in GI(Gastrointestinal) endoscopy, we propose a multi-domain GI dataset and a light, plug-in block called InvNorm(Invertible Normalization), which could achieve a better generalization performance in any structure.
1 code implementation • 23 Apr 2022 • Wei Shao, Zhiling Jin, Shuo Wang, Yufan Kang, Xiao Xiao, Hamid Menouar, Zhaofeng Zhang, Junshan Zhang, Flora Salim
To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure.
2 code implementations • 21 Apr 2022 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Archana Venkatachalapathy, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Alice Li, Shangru Li, Rama Chellappa
The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries.
1 code implementation • 20 Apr 2022 • Yanbin Hao, Shuo Wang, Pei Cao, Xinjian Gao, Tong Xu, Jinmeng Wu, Xiangnan He
Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts.
1 code implementation • 17 Apr 2022 • Mohammed Shaiqur Rahman, Archana Venkatachalapathy, Anuj Sharma, Jiyang Wang, Senem Velipasalar Gursoy, David Anastasiu, Shuo Wang
The order and duration of each activity for each participant are random.
no code implementations • 3 Apr 2022 • Alsharif Abuadbba, Shuo Wang, Mahathir Almashor, Muhammed Ejaz Ahmed, Raj Gaire, Seyit Camtepe, Surya Nepal
However, with an average of 10K phishing links reported per hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of such ML-based solutions are laid bare.
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
1 code implementation • ACL 2022 • Shuo Wang, Zhixing Tan, Yang Liu
In this work, we propose to open this black box by directly integrating the constraints into NMT models.
no code implementations • 21 Mar 2022 • Shuo Wang, Sharif Abuadbba, Sidharth Agarwal, Kristen Moore, Ruoxi Sun, Minhui Xue, Surya Nepal, Seyit Camtepe, Salil Kanhere
Existing integrity verification approaches for deep models are designed for private verification (i. e., assuming the service provider is honest, with white-box access to model parameters).
2 code implementations • CVPR 2022 • Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, Yang You
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
no code implementations • 18 Jan 2022 • Hengrui Zhang, Youfang Lin, Sheng Han, Shuo Wang, Kai Lv
Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process.
Distributional Reinforcement Learning
reinforcement-learning
+1
1 code implementation • 11 Jan 2022 • Hanrui Wang, Shuo Wang, Zhe Jin, Yandan Wang, Cunjian Chen, Massimo Tistarell
This technique applies to both white-box and gray-box attacks against authentication systems that determine genuine or imposter users using the dissimilarity score.
1 code implementation • 19 Dec 2021 • Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Datwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gomez, Pablo Arbelaez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-min Pei, Murat AK, Sarahi Rosas-Gonzalez, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Lofstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andre Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel
In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.
no code implementations • 29 Nov 2021 • Xiaobo Hu, Zhihao Wu, Kai Lv, Shuo Wang, Youfang Lin
In the navigation task, we introduce an Agent-Centric Relation Graph (ACRG) for learning the visual representation based on the relationships in the environment.
1 code implementation • ACL 2022 • Zhixing Tan, Xiangwen Zhang, Shuo Wang, Yang Liu
Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks.
no code implementations • ICLR 2022 • Jiayi Li, Tao Lu, Xiaoge Cao, Yinghao Cai, Shuo Wang
Our approach relies only on human videos and does not require robot demonstration, which facilitates data collection and is more in line with human imitation behavior.
1 code implementation • 7 Aug 2021 • Chen Chen, Chen Qin, Cheng Ouyang, Zeju Li, Shuo Wang, Huaqi Qiu, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.
no code implementations • 16 Jul 2021 • Nicolo Savioli, Antonio de Marvao, Wenjia Bai, Shuo Wang, Stuart A. Cook, Calvin W. L. Chin, Daniel Rueckert, Declan P. O'Regan
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion.
no code implementations • 8 Jul 2021 • Shuo Wang, Chen Qin, Nicolo Savioli, Chen Chen, Declan O'Regan, Stuart Cook, Yike Guo, Daniel Rueckert, Wenjia Bai
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.
no code implementations • 25 Jun 2021 • Shuo Wang, Zhaopeng Tu, Zhixing Tan, Wenxuan Wang, Maosong Sun, Yang Liu
Inspired by the recent progress of large-scale pre-trained language models on machine translation in a limited scenario, we firstly demonstrate that a single language model (LM4MT) can achieve comparable performance with strong encoder-decoder NMT models on standard machine translation benchmarks, using the same training data and similar amount of model parameters.
no code implementations • Findings (ACL) 2021 • Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu
Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice.
1 code implementation • CVPR 2022 • Kai Wang, Shuo Wang, Panpan Zhang, Zhipeng Zhou, Zheng Zhu, Xiaobo Wang, Xiaojiang Peng, Baigui Sun, Hao Li, Yang You
This method adopts Dynamic Class Pool (DCP) for storing and updating the identities features dynamically, which could be regarded as a substitute for the FC layer.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
no code implementations • 10 May 2021 • Shuo Wang, Lingjuan Lyu, Surya Nepal, Carsten Rudolph, Marthie Grobler, Kristen Moore
We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations (NaTra) of the inputs, which are then used to augment the training dataset of the image classifier.
no code implementations • 3 May 2021 • Shuo Wang, Surya Nepal, Kristen Moore, Marthie Grobler, Carsten Rudolph, Alsharif Abuadbba
We introduce a new distributed/collaborative learning scheme to address communication overhead via latent compression, leveraging global data while providing privatization of local data without additional cost due to encryption or perturbation.
1 code implementation • 25 Apr 2021 • Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Yue Yao, Liang Zheng, Pranamesh Chakraborty, Christian E. Lopez, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff
Track 3 addressed city-scale multi-target multi-camera vehicle tracking.
no code implementations • 1 Apr 2021 • Jingfeng Lu, Shuo Wang, Ping Li, Dong Ye
Low-dose computed tomography (CT) allows the reduction of radiation risk in clinical applications at the expense of image quality, which deteriorates the diagnosis accuracy of radiologists.
no code implementations • 29 Mar 2021 • Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo
To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.
no code implementations • 28 Mar 2021 • Min Wang, Shanchen Pang, Tong Ding, Sibo Qiao, Xue Zhai, Shuo Wang, Neal N. Xiong, Zhengwen Huang
In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).
2 code implementations • 10 Mar 2021 • Hainan Li, Renshuai Tao, Jun Li, Haotong Qin, Yifu Ding, Shuo Wang, Xianglong Liu
Self-supervised learning is emerged as an efficient method to utilize unlabeled data.
no code implementations • 10 Feb 2021 • Pan Wang, Rui Zhou, Shuo Wang, Ling Li, Wenjia Bai, Jialu Fan, Chunlin Li, Peter Childs, Yike Guo
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.
no code implementations • 20 Jan 2021 • Tao Wei, Angelica I Aviles-Rivero, Shuo Wang, Yuan Huang, Fiona J Gilbert, Carola-Bibiane Schönlieb, Chang Wen Chen
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
no code implementations • 1 Jan 2021 • Xiaolei Hua, Su Wang, Lin Zhu, Dong Zhou, Junlan Feng, Yiting Wang, Chao Deng, Shuo Wang, Mingtao Mei
However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.
1 code implementation • ICCV 2021 • Shu Yang, Lu Zhang, Jinqing Qi, Huchuan Lu, Shuo Wang, Xiaoxing Zhang
How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation.
Semantic Segmentation
Unsupervised Video Object Segmentation
+2
no code implementations • 31 Dec 2020 • Zhixing Tan, Shuo Wang, Zonghan Yang, Gang Chen, Xuancheng Huang, Maosong Sun, Yang Liu
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers.
1 code implementation • 27 Dec 2020 • Xudong Lv, Boya Wang, Dong Ye, Shuo Wang
In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors.
no code implementations • 16 Nov 2020 • Boyao Li, Tao Lu, Jiayi Li, Ning Lu, Yinghao Cai, Shuo Wang
Exploration in environments with sparse feedback remains a challenging research problem in reinforcement learning (RL).
no code implementations • 14 Oct 2020 • Xudong Lv, Boya Wang, Dong Ye, Shuo Wang
In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions.
no code implementations • 26 Aug 2020 • Shan Jia, Shuo Wang, Chuanbo Hu, Paula Webster, Xin Li
Facial expressions of emotion play an important role in human social interactions.
no code implementations • 14 Aug 2020 • Ye Bi, Shuo Wang, Zhongrui Fan
The cataloging of product listings is a fundamental problem for most e-commerce platforms.
no code implementations • 14 Aug 2020 • Ye Bi, Shuo Wang, Zhongrui Fan
The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion.
1 code implementation • ICML 2020 • Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, Tao Mei
In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.
no code implementations • 26 Jun 2020 • Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.
1 code implementation • 23 Jun 2020 • Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.
no code implementations • 23 Jun 2020 • Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai
Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.
no code implementations • 17 Jun 2020 • Shuo Wang, Surya Nepal, Alsharif Abuadbba, Carsten Rudolph, Marthie Grobler
The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e. g., slightly changing the facial expression of human portraits.
1 code implementation • 8 Jun 2020 • Chen Qin, Shuo Wang, Chen Chen, Huaqi Qiu, Wenjia Bai, Daniel Rueckert
The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible.
1 code implementation • ACL 2020 • Shuo Wang, Zhaopeng Tu, Shuming Shi, Yang Liu
Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output.
3 code implementations • ICCV 2019 • Zheng Tang, Milind Naphade, Stan Birchfield, Jonathan Tremblay, William Hodge, Ratnesh Kumar, Shuo Wang, Xiaodong Yang
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.
no code implementations • 30 Apr 2020 • Milind Naphade, Shuo Wang, David Anastasiu, Zheng Tang, Ming-Ching Chang, Xiaodong Yang, Liang Zheng, Anuj Sharma, Rama Chellappa, Pranamesh Chakraborty
Track 3 addressed city-scale multi-target multi-camera vehicle tracking.
1 code implementation • 25 Apr 2020 • Philip Nadler, Shuo Wang, Rossella Arcucci, Xian Yang, Yike Guo
We compare and discuss model results which conducts updates as new observations become available.
no code implementations • 22 Mar 2020 • Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen
Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.
no code implementations • 19 Mar 2020 • Yuanhan Mo, Shuo Wang, Chengliang Dai, Rui Zhou, Zhongzhao Teng, Wenjia Bai, Yike Guo
Supervised deep learning requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
2 code implementations • ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2020 • Shuo Wang, Yan-ran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao
When predicting PM2. 5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period.
1 code implementation • 8 Feb 2020 • Ke Li, Zilin Xiang, Tao Chen, Shuo Wang, Kay Chen Tan
Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.
no code implementations • 3 Feb 2020 • Shuo Wang, Tianle Chen, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen
In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.
no code implementations • MIDL 2019 • Yuanhan Mo, Shuo Wang, Chengliang Dai, Zhongzhao Teng, Wenjia Bai, Yike Guo
Supervised deep learning for medical imaging analysis requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for medical segmentation tasks), which are expensive and time-consuming to obtain.
no code implementations • 18 Jan 2020 • Shuo Wang, Tianle Chen, Shangyu Chen, Carsten Rudolph, Surya Nepal, Marthie Grobler
Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.
no code implementations • 18 Jan 2020 • Mengyuan Chen, Jiang Zhang, Zhang Zhang, Lun Du, Qiao Hu, Shuo Wang, Jiaqi Zhu
We carried out experiments on discrete and continuous time series data.
no code implementations • 10 Jan 2020 • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.
no code implementations • 6 Jan 2020 • Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, Tianle Chen
We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
no code implementations • 26 Nov 2019 • Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, Tao Mei
Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.
no code implementations • 26 Nov 2019 • Jin Xie, Longfei Wang, Paula Webster, Yang Yao, Jiayao Sun, Shuo Wang, Huihui Zhou
In this study, we developed a novel two-stream deep learning network for this recognition based on 700 images and corresponding eye movement patterns of ASD and TD, and obtained an accuracy of 0. 95, which was higher than the previous state-of-the-art.
no code implementations • 19 Nov 2019 • Shuo Wang, Chengliang Dai, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity.
no code implementations • 1 Nov 2019 • Ziyuan Pu, Zhiyong Cui, Shuo Wang, Qianmu Li, Yinhai Wang
The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.
no code implementations • 1 Nov 2019 • Ziyuan Pu, Shuo Wang, Chenglong Liu, Zhiyong Cui, Yinhai Wang
A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity.
no code implementations • 29 Sep 2019 • Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, Yun Liang
To achieve real-time, highly-efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and develop an efficient processing element (PE) structure supporting the heterogeneous weight quantization, CONV dataflow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource.
1 code implementation • IJCNLP 2019 • Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, Maosong Sun
While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy.
no code implementations • 29 Aug 2019 • Bang Wu, Shuo Wang, Xingliang Yuan, Cong Wang, Carsten Rudolph, Xiangwen Yang
To avoid the bloated ensemble size during inference, we propose a two-phase defence, in which inference from the Student model is firstly performed to narrow down the candidate differentiators to be assembled, and later only a small, fixed number of them can be chosen to validate clean or reject adversarial inputs effectively.
3 code implementations • 28 Jun 2019 • Xiaocan Li, Shuo Wang, Yinghao Cai
We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity.
no code implementations • 22 Jun 2019 • Maolin Shi, Shuo Wang, Wei Sun, Liye Lv, Xueguan Song
Computational simulations with different fidelity have been widely used in engineering design.
no code implementations • CVPR 2019 • Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David Anastasiu, Jenq-Neng Hwang
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
no code implementations • 19 Mar 2019 • Naijun Liu, Tao Lu, Yinghao Cai, Boyao Li, Shuo Wang
Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available.
no code implementations • 20 Jan 2019 • Shifeng Zhang, Rui Zhu, Xiaobo Wang, Hailin Shi, Tianyu Fu, Shuo Wang, Tao Mei, Stan Z. Li
With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.
1 code implementation • 30 Dec 2018 • Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin, Jiang Zhang
We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.
3 code implementations • 29 Dec 2018 • Xiaobo Wang, Shuo Wang, Shifeng Zhang, Tianyu Fu, Hailin Shi, Tao Mei
Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.
Ranked #1 on
Face Identification
on Trillion Pairs Dataset
no code implementations • CVPR 2018 • Tiantian Wang, Lihe Zhang, Shuo Wang, Huchuan Lu, Gang Yang, Xiang Ruan, Ali Borji
Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position.
Ranked #12 on
RGB Salient Object Detection
on DUTS-TE
no code implementations • 20 Mar 2018 • Zhe Li, Shuo Wang, Caiwen Ding, Qinru Qiu, Yanzhi Wang, Yun Liang
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.
no code implementations • 14 Mar 2018 • Shuo Wang, Zhe Li, Caiwen Ding, Bo Yuan, Yanzhi Wang, Qinru Qiu, Yun Liang
The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.
no code implementations • 28 Jul 2017 • Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao
It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift.
no code implementations • 20 Mar 2017 • Shuo Wang, Leandro L. Minku, Xin Yao
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift.
no code implementations • CVPR 2013 • Shuo Wang, Jungseock Joo, Yizhou Wang, Song-Chun Zhu
We evaluate the proposed method by (i) showing the improvement of attribute recognition accuracy; and (ii) comparing the average precision of localizing attributes to the scene parts.