Search Results for author: Shuo Wang

Found 168 papers, 67 papers with code

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 +1

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

The 8th AI City Challenge

no code implementations15 Apr 2024 Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Pranamesh Chakraborty, Sanjita Prajapati, Quan Kong, Norimasa Kobori, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Fady Alnajjar, Ganzorig Batnasan, Ping-Yang Chen, Jun-Wei Hsieh, Xunlei Wu, Sameer Satish Pusegaonkar, Yizhou Wang, Sujit Biswas, Rama Chellappa

The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities.

Dense Video Captioning

Technical Report: Masked Skeleton Sequence Modeling for Learning Larval Zebrafish Behavior Latent Embeddings

no code implementations23 Mar 2024 Lanxin Xu, Shuo Wang

In this report, we introduce a novel self-supervised learning method for extracting latent embeddings from behaviors of larval zebrafish.

Self-Supervised Learning Sentence

Boosting Few-Shot Learning via Attentive Feature Regularization

no code implementations23 Mar 2024 Xingyu Zhu, Shuo Wang, Jinda Lu, Yanbin Hao, Haifeng Liu, Xiangnan He

Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from different categories with a blending factor.

Few-Shot Learning

SoK: Can Trajectory Generation Combine Privacy and Utility?

1 code implementation12 Mar 2024 Erik Buchholz, Alsharif Abuadbba, Shuo Wang, Surya Nepal, Salil S. Kanhere

This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework.

Privacy Preserving

Say More with Less: Understanding Prompt Learning Behaviors through Gist Compression

1 code implementation25 Feb 2024 Xinze Li, Zhenghao Liu, Chenyan Xiong, Shi Yu, Yukun Yan, Shuo Wang, Ge Yu

It finetunes the compression plugin module and uses the representations of gist tokens to emulate the raw prompts in the vanilla language model.

Language Modelling

ActiveRAG: Revealing the Treasures of Knowledge via Active Learning

1 code implementation21 Feb 2024 Zhipeng Xu, Zhenghao Liu, Yibin Liu, Chenyan Xiong, Yukun Yan, Shuo Wang, Shi Yu, Zhiyuan Liu, Ge Yu

Retrieval Augmented Generation (RAG) has introduced a new paradigm for Large Language Models (LLMs), aiding in the resolution of knowledge-intensive tasks.

Active Learning Position +2

$\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens

1 code implementation21 Feb 2024 Xinrong Zhang, Yingfa Chen, Shengding Hu, Zihang Xu, JunHao Chen, Moo Khai Hao, Xu Han, Zhen Leng Thai, Shuo Wang, Zhiyuan Liu, Maosong Sun

Processing and reasoning over long contexts is crucial for many practical applications of Large Language Models (LLMs), such as document comprehension and agent construction.

OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models

1 code implementation21 Feb 2024 Meng Xu, Shuo Wang, Liner Yang, Haoyu Wang, Zhenghao Liu, Cunliang Kong, Yun Chen, Yang Liu, Maosong Sun, Erhong Yang

We evaluate several representative multilingual LLMs on the proposed OMGEval, which we believe will provide a valuable reference for the community to further understand and improve the multilingual capability of LLMs.

General Knowledge Logical Reasoning

Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages

1 code implementation19 Feb 2024 Yuanchi Zhang, Yile Wang, Zijun Liu, Shuo Wang, Xiaolong Wang, Peng Li, Maosong Sun, Yang Liu

While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages.

Transfer Learning

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization

1 code implementation18 Feb 2024 Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun

Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns.

Code Generation Data Visualization

LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks

no code implementations18 Feb 2024 Hanqing Wang, Bowen Ping, Shuo Wang, Xu Han, Yun Chen, Zhiyuan Liu, Maosong Sun

Most prior works on LoRA combination primarily rely on task-level weights for each involved LoRA, making different examples and tokens share the same LoRA weights.

Math

OneBit: Towards Extremely Low-bit Large Language Models

no code implementations17 Feb 2024 Yuzhuang Xu, Xu Han, Zonghan Yang, Shuo Wang, Qingfu Zhu, Zhiyuan Liu, Weidong Liu, Wanxiang Che

Model quantification uses low bit-width values to represent the weight matrices of models, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs.

Quantization

UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset

1 code implementation7 Feb 2024 Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun

Different from previous works that simply translate English instructions, we consider both the language-specific and language-agnostic abilities of LLMs.

Cross-Lingual Transfer Data Augmentation

Learning with Mixture of Prototypes for Out-of-Distribution Detection

1 code implementation5 Feb 2024 Haodong Lu, Dong Gong, Shuo Wang, Jason Xue, Lina Yao, Kristen Moore

To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +1

LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning

no code implementations27 Jan 2024 Pengjie Liu, Zhenghao Liu, Xiaoyuan Yi, Liner Yang, Shuo Wang, Yu Gu, Ge Yu, Xing Xie, Shuang-Hua Yang

It proposes a dual-view legal clue reasoning mechanism, which derives from two reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments according to the judgment experiences learned from analogy/confusing legal cases; 2) Legal Ground Reasoning, which lies in matching the legal clues between criminal cases and legal decisions.

Stream Query Denoising for Vectorized HD Map Construction

no code implementations17 Jan 2024 Shuo Wang, Fan Jia, Yingfei Liu, Yucheng Zhao, Zehui Chen, Tiancai Wang, Chi Zhang, Xiangyu Zhang, Feng Zhao

This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction.

Autonomous Driving Denoising

Graph Elimination Networks

no code implementations2 Jan 2024 Shuo Wang, Ge Cheng, Yun Zhang

Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers.

GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks

1 code implementation13 Dec 2023 Bang Wu, He Zhang, Xiangwen Yang, Shuo Wang, Minhui Xue, Shirui Pan, Xingliang Yuan

These limitations call for an effective and comprehensive solution that detects and mitigates data misuse without requiring exact training data while respecting the proprietary nature of such data.

Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification

no code implementations12 Dec 2023 Yuwei Guo, WenHao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu

Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities.

Data Augmentation Person Re-Identification

CAR: Consolidation, Augmentation and Regulation for Recipe Retrieval

no code implementations8 Dec 2023 Fangzhou Song, Bin Zhu, Yanbin Hao, Shuo Wang, Xiangnan He

Learning recipe and food image representation in common embedding space is non-trivial but crucial for cross-modal recipe retrieval.

Retrieval

Building Category Graphs Representation with Spatial and Temporal Attention for Visual Navigation

no code implementations6 Dec 2023 Xiaobo Hu, Youfang Lin, Hehe Fan, Shuo Wang, Zhihao Wu, Kai Lv

To this end, an agent needs to 1) learn a piece of certain knowledge about the relations of object categories in the world during training and 2) look for the target object based on the pre-learned object category relations and its moving trajectory in the current unseen environment.

Object Visual Navigation

A Reliable Representation with Bidirectional Transition Model for Visual Reinforcement Learning Generalization

no code implementations4 Dec 2023 Xiaobo Hu, Youfang Lin, Yue Liu, Jinwen Wang, Shuo Wang, Hehe Fan, Kai Lv

Visual reinforcement learning has proven effective in solving control tasks with high-dimensional observations.

INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair

1 code implementation16 Nov 2023 Hanbin Wang, Zhenghao Liu, Shuo Wang, Ganqu Cui, Ning Ding, Zhiyuan Liu, Ge Yu

INTERVENOR prompts Large Language Models (LLMs) to play distinct roles during the code repair process, functioning as both a Code Learner and a Code Teacher.

Code Repair Code Translation

TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding

1 code implementation6 Nov 2023 Shuo Wang, Jing Li, Zibo Zhao, Dongze Lian, Binbin Huang, Xiaomei Wang, Zhengxin Li, Shenghua Gao

Holistic scene understanding includes semantic segmentation, surface normal estimation, object boundary detection, depth estimation, etc.

Boundary Detection Depth Estimation +5

Understanding Parameter Saliency via Extreme Value Theory

no code implementations27 Oct 2023 Shuo Wang, Issei Sato

Furthermore, we show that the existing parameter saliency method exhibits a bias against the depth of layers in deep neural networks.

Anomaly Detection Saliency Ranking

Breaking of brightness consistency in optical flow with a lightweight CNN network

1 code implementation24 Oct 2023 Yicheng Lin, Shuo Wang, Yunlong Jiang, Bin Han

Modifying the typical brightness consistency of the optical flow method to the convolutional feature consistency yields the light-robust hybrid optical flow method.

Optical Flow Estimation

VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models

no code implementations20 Oct 2023 Shuo Wang, Keke Gai, Jing Yu, Liehuang Zhu, Kim-Kwang Raymond Choo, Bin Xiao

Then the passive party, who owns only features of the sample, injects the blinding factor into the local embedding and sends it to the active party.

Vertical Federated Learning

Action Recognition Utilizing YGAR Dataset

no code implementations2 Oct 2023 Shuo Wang, Amiya Ranjan, Lawrence Jiang

The scarcity of high quality actions video data is a bottleneck in the research and application of action recognition.

Action Recognition

Natural Language Models for Data Visualization Utilizing nvBench Dataset

no code implementations2 Oct 2023 Shuo Wang, Carlos Crespo-Quinones

Translation of natural language into syntactically correct commands for data visualization is an important application of natural language models and could be leveraged to many different tasks.

Data Visualization Natural Language Queries +1

RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

no code implementations9 Sep 2023 Yizhe Zhang, Shuo Wang, Yejia Zhang, Danny Z. Chen

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e. g., 99. 5\% of the time).

Conformal Prediction Decision Making +2

Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf

no code implementations9 Sep 2023 Yuzhuang Xu, Shuo Wang, Peng Li, Fuwen Luo, Xiaolong Wang, Weidong Liu, Yang Liu

Communication games, which we refer to as incomplete information games that heavily depend on natural language communication, hold significant research value in fields such as economics, social science, and artificial intelligence.

Retrieval

SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation

1 code implementation26 Aug 2023 Yizhe Zhang, Tao Zhou, Shuo Wang, Ye Wu, Pengfei Gu, Danny Z. Chen

Our new method is iterative and consists of two main stages: (1) segmentation model training; (2) expanding the labeled set by using the trained segmentation model, an unlabeled set, SAM, and domain-specific knowledge.

Image Segmentation Lesion Segmentation +3

A Unified Query-based Paradigm for Camouflaged Instance Segmentation

1 code implementation14 Aug 2023 Bo Dong, Jialun Pei, Rongrong Gao, Tian-Zhu Xiang, Shuo Wang, Huan Xiong

Due to the high similarity between camouflaged instances and the background, the recently proposed camouflaged instance segmentation (CIS) faces challenges in accurate localization and instance segmentation.

Boundary Detection Instance Segmentation +3

Pluggable Neural Machine Translation Models via Memory-augmented Adapters

1 code implementation12 Jul 2023 Yuzhuang Xu, Shuo Wang, Peng Li, Xuebo Liu, Xiaolong Wang, Weidong Liu, Yang Liu

Although neural machine translation (NMT) models perform well in the general domain, it remains rather challenging to control their generation behavior to satisfy the requirement of different users.

Machine Translation NMT +1

Synthetic Demographic Data Generation for Card Fraud Detection Using GANs

1 code implementation29 Jun 2023 Shuo Wang, Terrence Tricco, Xianta Jiang, Charles Robertson, John Hawkin

This study can help improve the cognition of synthetic data and further explore the application of synthetic data generation in card fraud detection.

Fraud Detection Generative Adversarial Network +1

Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots

1 code implementation23 Jun 2023 Nan Hu, Daobilige Su, Shuo Wang, Xuechang Wang, Huiyu Zhong, Zimeng Wang, Yongliang Qiao, Yu Tan

Regarding the robust tracking of vegetable plants, to solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel method of Multiple Object Tracking and Segmentation (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants.

Instance Segmentation Multiple Object Tracking +3

MCTS: A Multi-Reference Chinese Text Simplification Dataset

1 code implementation5 Jun 2023 Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang

We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research.

Machine Translation Text Simplification

IDLL: Inverse Depth Line based Visual Localization in Challenging Environments

no code implementations23 Apr 2023 Wanting Li, Yu Shao, Yongcai Wang, Shuo Wang, Xuewei Bai, Deying Li

In this paper, we propose Inverse Depth Line Localization(IDLL), which models each extracted line feature using two inverse depth variables exploiting the fact that the projected pixel coordinates on the image plane are rather accurate, which partially restrict the lines.

Visual Localization

AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports

no code implementations23 Apr 2023 Hongyu Sun, Yongcai Wang, Xudong Cai, Peng Wang, Zhe Huang, Deying Li, Yu Shao, Shuo Wang

To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118, 312 time-series images, where a total of 409, 967 bounding boxes of flying birds are manually, carefully annotated.

Time Series

The 7th AI City Challenge

no code implementations15 Apr 2023 Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Qi Feng, Vitaly Ablavsky, Stan Sclaroff, Pranamesh Chakraborty, Sanjita Prajapati, Alice Li, Shangru Li, Krishna Kunadharaju, Shenxin Jiang, Rama Chellappa

The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems (ITS) - that have considerable untapped potential.

Retrieval

CamDiff: Camouflage Image Augmentation via Diffusion Model

1 code implementation11 Apr 2023 Xue-Jing Luo, Shuo Wang, Zongwei Wu, Christos Sakaridis, Yun Cheng, Deng-Ping Fan, Luc van Gool

Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.

Image Augmentation Image Classification +3

Bi-directional Distribution Alignment for Transductive Zero-Shot Learning

1 code implementation CVPR 2023 Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang, Xiangnan He

It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match.

Zero-Shot Learning

Toward a Geometric Theory of Manifold Untangling

no code implementations7 Mar 2023 Xin Li, Shuo Wang

It has been hypothesized that the ventral stream processing for object recognition is based on a mechanism called cortically local subspace untangling.

Object Object Recognition

Toward NeuroDM: Where Computational Neuroscience Meets Data Mining

no code implementations7 Mar 2023 Xin Li, Bin Liu, Shuo Wang

At the intersection of computational neuroscience (CN) and data mining (DM), we advocate a holistic view toward their rich connections.

Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View

no code implementations CVPR 2023 Shuo Wang, Xinhai Zhao, Hai-Ming Xu, Zehui Chen, Dameng Yu, Jiahao Chang, Zhen Yang, Feng Zhao

Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation.

3D Object Detection Depth Estimation +3

Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

2 code implementations28 Feb 2023 Peng Zheng, Jie Qin, Shuo Wang, Tian-Zhu Xiang, Huan Xiong

To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories.

Co-Salient Object Detection object-detection +1

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy

1 code implementation30 Jan 2023 Mengyun Qiao, Shuo Wang, Huaqi Qiu, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Wenjia Bai

Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases.

Anatomy Image Segmentation +1

Boosting Whole Slide Image Classification from the Perspectives of Distribution, Correlation and Magnification

no code implementations ICCV 2023 Linhao Qu, Zhiwei Yang, Minghong Duan, Yingfan Ma, Shuo Wang, Manning Wang, Zhijian Song

However, there are still three important issues that have not been fully addressed: (1) positive bags with a low positive instance ratio are prone to the influence of a large number of negative instances; (2) the correlation between local and global features of pathology images has not been fully modeled; and (3) there is a lack of effective information interaction between different magnifications.

Image Classification Multiple Instance Learning

EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)

no code implementations23 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.

Anomaly Detection Density Estimation

DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks

no code implementations24 Nov 2022 Seonhye Park, Alsharif Abuadbba, Shuo Wang, Kristen Moore, Yansong Gao, Hyoungshick Kim, Surya Nepal

In this study, we introduce DeepTaster, a novel DNN fingerprinting technique, to address scenarios where a victim's data is unlawfully used to build a suspect model.

Data Augmentation Transfer Learning

Multitask Learning for Improved Late Mechanical Activation Detection of Heart from Cine DENSE MRI

no code implementations11 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.

Joint Deep Learning for Improved Myocardial Scar Detection from Cardiac MRI

no code implementations11 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.

Myocardium Segmentation Segmentation

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

1 code implementation28 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.

Anatomy

Improved post-hoc probability calibration for out-of-domain MRI segmentation

1 code implementation4 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.

Image Segmentation MRI segmentation +2

Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP

1 code implementation15 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.

TANet: Transformer-based Asymmetric Network for RGB-D Salient Object Detection

1 code implementation4 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.

object-detection RGB-D Salient Object Detection +1

Trichomonas Vaginalis Segmentation in Microscope Images

no code implementations3 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.

Object object-detection +2

Boundary-Guided Camouflaged Object Detection

1 code implementation2 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.

Object object-detection +2

Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior

1 code implementation8 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.

Image Registration

Suggestive Annotation of Brain MR Images with Gradient-guided Sampling

no code implementations2 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.

Brain Segmentation Image Segmentation +2

A Template-based Method for Constrained Neural Machine Translation

1 code implementation23 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.

Machine Translation NMT +1

Understanding and Mitigating the Uncertainty in Zero-Shot Translation

no code implementations20 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.

Machine Translation Translation

DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learning

no code implementations11 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.

Object

InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy

no code implementations5 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.

Domain Generalization Ethics +3

Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

1 code implementation23 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.

Graph Attention Spatio-Temporal Forecasting

Attention in Attention: Modeling Context Correlation for Efficient Video Classification

1 code implementation20 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.

Video Classification

Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver

1 code implementation17 Apr 2022 Mohammed Shaiqur Rahman, Jiyang Wang, Senem Velipasalar Gursoy, David Anastasiu, Shuo Wang, Anuj Sharma

This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones.

BIG-bench Machine Learning

Towards Web Phishing Detection Limitations and Mitigation

no code implementations3 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.

Attribute

High-resolution Iterative Feedback Network for Camouflaged Object Detection

1 code implementation22 Mar 2022 Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Ying Tai, Chengjie Wang, Ling Shao

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.

Object object-detection +2

PublicCheck: Public Integrity Verification for Services of Run-time Deep Models

no code implementations21 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).

Model Compression

CAFE: Learning to Condense Dataset by Aligning Features

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.

Dataset Condensation

Conservative Distributional Reinforcement Learning with Safety Constraints

no code implementations18 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

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

1 code implementation11 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.

Adversarial Attack Face Recognition

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

1 code implementation19 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.

Benchmarking Brain Tumor Segmentation +5

Agent-Centric Relation Graph for Object Visual Navigation

no code implementations29 Nov 2021 Xiaobo Hu, Youfang Lin, Shuo Wang, Zhihao Wu, Kai Lv

ACRG is a highly effective structure that consists of two relationships, i. e., the horizontal relationship among objects and the distance relationship between the agent and objects .

Object Relation +1

Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors

1 code implementation19 Nov 2021 Ruoxi Sun, Minhui Xue, Gareth Tyson, Tian Dong, Shaofeng Li, Shuo Wang, Haojin Zhu, Seyit Camtepe, Surya Nepal

We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i. e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i. e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks.

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

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.

Machine Translation Translation

Meta-Imitation Learning by Watching Video Demonstrations

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.

Imitation Learning

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.

Anatomy Cardiac Segmentation +2

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 +2

On the Language Coverage Bias for Neural Machine Translation

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.

Data Augmentation Machine Translation +3

An Efficient Training Approach for Very Large Scale Face Recognition

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)

Face Recognition Face Verification

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.

Attribute Data Augmentation +2

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/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.

Disentanglement 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.

Management Time Series +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 +2

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).

FetchPush-v1 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.

BIG-bench Machine Learning Image Segmentation +2

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 Image Segmentation +3

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.

Image Segmentation MRI segmentation +2

Adversarial Defense by Latent Style Transformations

no code implementations17 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.

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 NMT +1

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 regression +1

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 +2

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

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.

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

Suggestive Labelling for Medical Image Analysis by Adaptive Latent Space Sampling

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.

Informativeness

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 Disentanglement

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}.

Electrocardiography (ECG) Electroencephalogram (EEG) +3

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

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

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.

Friction