Search Results for author: Shuo Wang

Found 69 papers, 18 papers with code

Dual Adversarial Network for Deep Active Learning

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.

Active Learning

Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery

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.

Few-Shot Learning

Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition

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.

Face Recognition Knowledge Distillation

MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators

no code implementations13 Oct 2021 Zhixing Tan, Xiangwen Zhang, Shuo Wang, Yang Liu

During each stage, we independently apply different continuous prompts for allowing pre-trained language models better adapting to translation tasks.

Machine Translation Translation

Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation

1 code implementation7 Aug 2021 Chen Chen, Chen Qin, Cheng Ouyang, 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.

Cardiac Segmentation Data Augmentation

Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

no code implementations8 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.

Cardiac Segmentation Super-Resolution

Language Models are Good Translators

no code implementations25 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.

Language Modelling Machine Translation +1

On the Language Coverage Bias for Neural Machine Translation

no code implementations7 Jun 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.

Data Augmentation Machine Translation +1

An Efficient Training Approach for Very Large Scale Face Recognition

1 code implementation21 May 2021 Kai Wang, Shuo Wang, Zhipeng Zhou, Xiaobo Wang, Xiaojiang Peng, Baigui Sun, Hao Li, Yang You

After that, to reduce the training time and hardware costs of the FC layer, we propose a Dynamic Class Pool (DCP) that stores the features from Gallery Net and calculates the inner product (logits) with positive samples (whose identities are in the DCP) in each mini-batch.

Face Recognition

Robust Training Using Natural Transformation

no code implementations10 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.

Data Augmentation Image Classification +1

OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning

no code implementations3 May 2021 Shuo Wang, Surya Nepal, Kristen Moore, Marthie Grobler, Carsten Rudolph, Alsharif Abuadbba

We introduce a new distributed learning scheme to address communication overhead via latent compression, leveraging global data while providing local privatization of local data without additional cost due to encryption or perturbation.

Federated Learning

High-quality Low-dose CT Reconstruction Using Convolutional Neural Networks with Spatial and Channel Squeeze and Excitation

no code implementations1 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.

Computed Tomography (CT) Image Reconstruction

Product semantics translation from brain activity via adversarial learning

no code implementations29 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.

EEG Translation

IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT

no code implementations28 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).

Few-Shot Learning

A General Framework for Revealing Human Mind with auto-encoding GANs

no code implementations10 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.

Brain Decoding

GenAD: General Representations of Multivariate Time Series for Anomaly Detection

no code implementations1 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.

Time Series Unsupervised Anomaly Detection

Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation

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 Video Object Segmentation +2

Neural Machine Translation: A Review of Methods, Resources, and Tools

no code implementations31 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.

Data Augmentation Machine Translation +1

LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network

1 code implementation27 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.

Translation

ACDER: Augmented Curiosity-Driven Experience Replay

no code implementations16 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).

Semantic Flow-guided Motion Removal Method for Robust Mapping

no code implementations14 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.

Instance Segmentation Optical Flow Estimation +1

Detection of Genuine and Posed Facial Expressions of Emotion: A Review

no code implementations26 Aug 2020 Shan Jia, Shuo Wang, Chuanbo Hu, Paula Webster, Xin Li

Facial expressions of emotion play an important role in human social interactions.

A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter

no code implementations14 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.

Learning-To-Rank

A Multimodal Late Fusion Model for E-Commerce Product Classification

no code implementations14 Aug 2020 Ye Bi, Shuo Wang, Zhongrui Fan

The cataloging of product listings is a fundamental problem for most e-commerce platforms.

Classification General Classification

Loss Function Search for Face Recognition

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.

AutoML Face Recognition

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

no code implementations26 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.

Semantic Segmentation

Realistic Adversarial Data Augmentation for MR Image Segmentation

1 code implementation23 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.

Data Augmentation Medical Image Segmentation

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 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.

MRI segmentation

Adversarial Defense by Latent Style Transformations

no code implementations17 Jun 2020 Shuo Wang, Surya Nepal, Marthie Grobler, Carsten Rudolph, Tianle Chen, Shangyu Chen

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.

Adversarial Defense

Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

1 code implementation8 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.

Image Registration

On the Inference Calibration of Neural Machine Translation

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.

Machine Translation Translation

An Epidemiological Modelling Approach for Covid19 via Data Assimilation

1 code implementation25 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.

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

no code implementations22 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.

MULTI-VIEW LEARNING Representation Learning

Efficient Deep Representation Learning by Adaptive Latent Space Sampling

no code implementations19 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.

General Classification Image Classification +1

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

1 code implementation10 Feb 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.

Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study

1 code implementation8 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.

Transfer Learning

Defending Adversarial Attacks via Semantic Feature Manipulation

no code implementations3 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.

General Classification

OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning

no code implementations18 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.

Anomaly Detection

Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models

no code implementations10 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}.

EEG Electrocardiography (ECG) +2

A Two-stream End-to-End Deep Learning Network for Recognizing Atypical Visual Attention in Autism Spectrum Disorder

no code implementations26 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.

Classification General Classification

Mis-classified Vector Guided Softmax Loss for Face Recognition

no code implementations26 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.

Face Recognition

Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data

no code implementations1 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.

Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

no code implementations1 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.

Decision Making Time Series

REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

no code implementations29 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.

Model Compression Object Detection +1

Improving Back-Translation with Uncertainty-based Confidence Estimation

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.

Low-Resource Neural Machine Translation Translation

Towards Defeating Misclassification Attacks Against Transfer Learning

no code implementations29 Aug 2019 Bang Wu, Shuo Wang, Xingliang Yuan, Cong Wang, Carsten Rudolph, Xiangwen Yang

To avoid the bloated ensemble size during inference, we propose 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.

Network Pruning Transfer Learning

Tutorial: Complexity analysis of Singular Value Decomposition and its variants

3 code implementations28 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.

A support vector regression-based multi-fidelity surrogate model

no code implementations22 Jun 2019 Maolin Shi, Shuo Wang, Wei Sun, Liye Lv, Xueguan Song

Computational simulations with different fidelity have been widely used in engineering design.

Hindsight Generative Adversarial Imitation Learning

no code implementations19 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.

Curriculum Learning Imitation Learning

Improved Selective Refinement Network for Face Detection

no code implementations20 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.

Data Augmentation Face Detection +1

A General Deep Learning Framework for Network Reconstruction and Dynamics Learning

1 code implementation30 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.

Time Series

Support Vector Guided Softmax Loss for Face Recognition

3 code implementations29 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.

Face Recognition

Detect Globally, Refine Locally: A Novel Approach to Saliency Detection

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.

RGB Salient Object Detection Saliency Detection +1

Efficient Recurrent Neural Networks using Structured Matrices in FPGAs

no code implementations20 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.

Model Compression Time Series

C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

no code implementations14 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.

Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)

no code implementations28 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.

A Systematic Study of Online Class Imbalance Learning with Concept Drift

no code implementations20 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.

Weakly Supervised Learning for Attribute Localization in Outdoor Scenes

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.

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