b'Hui Xue'

research

∙ 08/22/2023

Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging

Fast Adversarial Training (FAT) not only improves the model robustness b...

0 Xiaojun Jia, et al. ∙

research

∙ 07/24/2023

COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts

Practical object detection application can lose its effectiveness on ima...

0 Xiaofeng Mao, et al. ∙

research

∙ 07/24/2023

Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

Developing a practically-robust automatic speech recognition (ASR) is ch...

0 Gege Qi, et al. ∙

research

∙ 05/29/2023

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

Textual adversarial attacks can discover models' weaknesses by adding se...

0 Yangyi Chen, et al. ∙

research

∙ 05/26/2023

Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

Large language models (LLMs) have recently shown great potential for in-...

0 Ruixiang Tang, et al. ∙

research

∙ 05/20/2023

Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection

In this paper, we propose a joint generative and contrastive representat...

0 Xiao-Min Zeng, et al. ∙

research

∙ 04/14/2023

FairRec: Fairness Testing for Deep Recommender Systems

Deep learning-based recommender systems (DRSs) are increasingly and wide...

0 Huizhong Guo, et al. ∙

research

∙ 03/30/2023

ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing

Recent studies have shown that higher accuracy on ImageNet usually leads...

0 Xiaodan Li, et al. ∙

research

∙ 03/28/2023

TransAudio: Towards the Transferable Adversarial Audio Attack via Learning Contextualized Perturbations

In a transfer-based attack against Automatic Speech Recognition (ASR) sy...

0 Qi Gege, et al. ∙

research

∙ 03/24/2023

PIAT: Parameter Interpolation based Adversarial Training for Image Classification

Adversarial training has been demonstrated to be the most effective appr...

0 Kun He, et al. ∙

research

∙ 02/21/2023

Improving Scene Text Image Super-Resolution via Dual Prior Modulation Network

Scene text image super-resolution (STISR) aims to simultaneously increas...

0 Shipeng Zhu, et al. ∙

research

∙ 11/30/2022

Rethinking Out-of-Distribution Detection From a Human-Centric Perspective

Out-Of-Distribution (OOD) detection has received broad attention over th...

0 Yao Zhu, et al. ∙

research

∙ 11/29/2022

Context-Aware Robust Fine-Tuning

Contrastive Language-Image Pre-trained (CLIP) models have zero-shot abil...

0 Xiaofeng Mao, et al. ∙

research

∙ 11/20/2022

Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models

Conditioned diffusion models have demonstrated state-of-the-art text-to-...

0 Xichen Pan, et al. ∙

research

∙ 10/28/2022

RoChBert: Towards Robust BERT Fine-tuning for Chinese

Despite of the superb performance on a wide range of tasks, pre-trained ...

0 Zihan Zhang, et al. ∙

research

∙ 10/17/2022

Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation

Capturing emotions within a conversation plays an essential role in mode...

0 Xiaohui Song, et al. ∙

research

∙ 10/09/2022

Boosting Out-of-distribution Detection with Typical Features

Out-of-distribution (OOD) detection is a critical task for ensuring the ...

0 Yao Zhu, et al. ∙

research

∙ 09/16/2022

Enhance the Visual Representation via Discrete Adversarial Training

Adversarial Training (AT), which is commonly accepted as one of the most...

1 Xiaofeng Mao, et al. ∙

research

∙ 01/26/2022

An Automated Question-Answering Framework Based on Evolution Algorithm

Building a deep learning model for a Question-Answering (QA) task requir...

0 Sinan Tan, et al. ∙

research

∙ 10/15/2021

Adversarial Attacks on ML Defense Models Competition

Due to the vulnerability of deep neural networks (DNNs) to adversarial e...

0 Yinpeng Dong, et al. ∙

research

∙ 07/21/2021

DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework

In multimodal tasks, we find that the importance of text and image modal...

0 Haiwen Hong, et al. ∙

research

∙ 07/17/2021

RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition

In fine-grained image recognition (FGIR), the localization and amplifica...

0 Yunqing Hu, et al. ∙

research

∙ 05/17/2021

Rethinking the Design Principles of Robust Vision Transformer

Recent advances on Vision Transformers (ViT) have shown that self-attent...

0 Xiaofeng Mao, et al. ∙

research

∙ 03/04/2021

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

We study the query-based attack against image retrieval to evaluate its ...

0 Xiaodan Li, et al. ∙

research

∙ 03/02/2021

Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

The remarkable success in face forgery techniques has received considera...

0 Honggu Liu, et al. ∙

research

∙ 02/23/2021

Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation

Despite that deep neural networks (DNNs) have achieved enormous success ...

0 Jinfeng Li, et al. ∙

research

∙ 12/14/2020

The Open Brands Dataset: Unified brand detection and recognition at scale

Intellectual property protection(IPP) have received more and more attent...

0 Xuan Jin, et al. ∙

research

∙ 12/10/2020

Composite Adversarial Attacks

Adversarial attack is a technique for deceiving Machine Learning (ML) mo...

0 Xiaofeng Mao, et al. ∙

research

∙ 08/14/2020

Landmark detection in Cardiac Magnetic Resonance Imaging Using A Convolutional Neural Network

Purpose: To develop a convolutional neural network (CNN) solution for ro...

7 Hui Xue, et al. ∙

research

∙ 08/11/2020

Sharp Multiple Instance Learning for DeepFake Video Detection

With the rapid development of facial manipulation techniques, face forge...

2 Xiaodan Li, et al. ∙

research

∙ 07/03/2020

MIRA: Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks

We study the problem of deep recall model in industrial web search, whic...

0 Yusi Zhang, et al. ∙

research

∙ 06/09/2020

GAP++: Learning to generate target-conditioned adversarial examples

Adversarial examples are perturbed inputs which can cause a serious thre...

5 Xiaofeng Mao, et al. ∙

research

∙ 05/29/2020

Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

Localizing thoracic diseases on chest X-ray plays a critical role in cli...

11 Wenwu Ye, et al. ∙

research

∙ 02/07/2020

Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network

This paper strives to learn fine-grained fashion similarity. In this sim...

0 Zhe Ma, et al. ∙

research

∙ 01/06/2020

Deeper Insights into Weight Sharing in Neural Architecture Search

With the success of deep neural networks, Neural Architecture Search (NA...

19 Yuge Zhang, et al. ∙

research

∙ 12/18/2019

Class Regularization: Improve Few-shot Image Classification by Reducing Meta Shift

Few-shot image classification requires the classifier to robustly cope w...

0 Da Chen, et al. ∙

research

∙ 11/15/2019

AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients

Deep neural networks have been shown to be vulnerable to adversarial exa...

0 Xiaodan Li, et al. ∙

research

∙ 11/15/2019

Learning To Characterize Adversarial Subspaces

Deep Neural Networks (DNNs) are known to be vulnerable to the maliciousl...

0 Xiaofeng Mao, et al. ∙

research

∙ 11/15/2019

Self-supervised Adversarial Training

Recent work has demonstrated that neural networks are vulnerable to adve...

0 Kejiang Chen, et al. ∙

research

∙ 11/14/2019

Self-Supervised Learning For Few-Shot Image Classification

Few-shot image classification aims to classify unseen classes with limit...

1 Da Chen, et al. ∙

research

∙ 11/02/2019

Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning

Recent development of quantitative myocardial blood flow (MBF) mapping a...

20 Hui Xue, et al. ∙

research

∙ 10/16/2019

Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients

Quantification of myocardial perfusion has the potential to improve dete...

27 Hui Xue, et al. ∙

research

∙ 10/10/2019

An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation

Graph Convolutional Network (GCN) has attracted intensive interests rece...

28 Nezihe Merve Gürel, et al. ∙

research

∙ 06/02/2019

Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attribu...

0 Feng Mao, et al. ∙

research

∙ 03/18/2019

Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks

The task of Language-Based Image Editing (LBIE) aims at generating a tar...

0 Xiaofeng Mao, et al. ∙

research

∙ 01/20/2019

Deep Features Analysis with Attention Networks

Deep neural network models have recently draw lots of attention, as it c...

0 Shipeng Xie, et al. ∙

Hui Xue

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