Search Results for author: Hui Xue

Found 83 papers, 33 papers with code

On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models

no code implementations24 Jun 2024 Dongyang Li, Junbing Yan, Taolin Zhang, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue, Jun Huang

Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to user queries.

RAG Retrieval +1

UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding

1 code implementation24 Jun 2024 Dongyang Li, Taolin Zhang, Jiali Deng, Longtao Huang, Chengyu Wang, Xiaofeng He, Hui Xue

Specifically, to retrieve the tokens with similar meanings for the semantic data augmentation across different languages, we propose a sequential clustering process in 3 stages: within a single language, across multiple languages of a language family, and across languages from multiple language families.

Data Augmentation Natural Language Understanding +2

KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning

1 code implementation24 Jun 2024 Dongyang Li, Taolin Zhang, Longtao Huang, Chengyu Wang, Xiaofeng He, Hui Xue

Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via self-supervised learning.

Hierarchical Reinforcement Learning Knowledge Graphs +4

Recurrent Inference Machine for Medical Image Registration

no code implementations19 Jun 2024 Yi Zhang, Yidong Zhao, Hui Xue, Peter Kellman, Stefan Klein, Qian Tao

Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.

Image Registration Medical Image Registration +1

DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models

1 code implementation31 May 2024 Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue, Jun Huang

(2) Considering that auxiliary parameters are required to store the knowledge for sequential editing, we construct a new dataset named \textbf{DAFSet}, fulfilling recent, popular, long-tail and robust properties to enhance the generality of sequential editing.

Hallucination Model Editing

Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning

no code implementations6 May 2024 Qizhou Chen, Taolin Zhang, Xiaofeng He, Dongyang Li, Chengyu Wang, Longtao Huang, Hui Xue

Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining.

knowledge editing Retrieval

R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models

no code implementations4 May 2024 Taolin Zhang, Dongyang Li, Qizhou Chen, Chengyu Wang, Longtao Huang, Hui Xue, Xiaofeng He, Jun Huang

The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement.

Graph Attention Hallucination +5

Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imaging

1 code implementation30 Apr 2024 Zheren Zhu, Azaan Rehman, Xiaozhi Cao, Congyu Liao, Yoo Jin Lee, Michael Ohliger, Hui Xue, Yang Yang

Recent developments in low-field (LF) magnetic resonance imaging (MRI) systems present remarkable opportunities for affordable and widespread MRI access.

Denoising Diversity

Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation

no code implementations6 Apr 2024 Azaan Rehman, Alexander Zhovmer, Ryo Sato, Yosuke Mukoyama, Jiji Chen, Alberto Rissone, Rosa Puertollano, Harshad Vishwasrao, Hari Shroff, Christian A. Combs, Hui Xue

Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization.

Image Denoising

Inline AI: Open-source Deep Learning Inference for Cardiac MR

no code implementations3 Apr 2024 Hui Xue, Rhodri H Davies, James Howard, Hunain Shiwani, Azaan Rehman, Iain Pierce, Henry Procter, Marianna Fontana, James C Moon, Eylem Levelt, Peter Kellman

The model was loaded and inference on incoming images were performed while the data acquisition was ongoing, and results were sent back to scanner.


Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model

no code implementations17 Mar 2024 Kangyang Xie, BinBin Yang, Hao Chen, Meng Wang, Cheng Zou, Hui Xue, Ming Yang, Chunhua Shen

Beyond the superiority of the text-to-image diffusion model in generating high-quality images, recent studies have attempted to uncover its potential for adapting the learned semantic knowledge to visual perception tasks.

Image Generation

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts

1 code implementation NeurIPS 2023 Zeyang Zhang, Xin Wang, Ziwei Zhang, Zhou Qin, Weigao Wen, Hui Xue, Haoyang Li, Wenwu Zhu

In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.

Link Prediction Node Classification

Understanding the Weakness of Large Language Model Agents within a Complex Android Environment

1 code implementation9 Feb 2024 Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, Zhen Xiao

These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system.

Date Understanding Language Modelling +1

Text Image Inpainting via Global Structure-Guided Diffusion Models

1 code implementation26 Jan 2024 Shipeng Zhu, Pengfei Fang, Chenjie Zhu, Zuoyan Zhao, Qiang Xu, Hui Xue

Leveraging the global structure of the text as a prior, the proposed GSDM develops an efficient diffusion model to recover clean texts.

Image Inpainting Scene Text Recognition

One-dimensional Adapter to Rule Them All: Concepts Diffusion Models and Erasing Applications

no code implementations CVPR 2024 Mengyao Lyu, Yuhong Yang, Haiwen Hong, Hui Chen, Xuan Jin, Yuan He, Hui Xue, Jungong Han, Guiguang Ding

The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.

Text-to-Image Generation

One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications

1 code implementation26 Dec 2023 Mengyao Lyu, Yuhong Yang, Haiwen Hong, Hui Chen, Xuan Jin, Yuan He, Hui Xue, Jungong Han, Guiguang Ding

The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.

Text-to-Image Generation

PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution

no code implementations29 Nov 2023 Zuoyan Zhao, Hui Xue, Pengfei Fang, Shipeng Zhu

Scene text image super-resolution (STISR) aims at simultaneously increasing the resolution and readability of low-resolution scene text images, thus boosting the performance of the downstream recognition task.

Image Super-Resolution Multi-Task Learning

DoubleAUG: Single-domain Generalized Object Detector in Urban via Color Perturbation and Dual-style Memory

no code implementations22 Nov 2023 Lei Qi, Peng Dong, Tan Xiong, Hui Xue, Xin Geng

In this paper, we aim to solve the single-domain generalizable object detection task in urban scenarios, meaning that a model trained on images from one weather condition should be able to perform well on images from any other weather conditions.

Autonomous Driving object-detection +1

Model Inversion Attack via Dynamic Memory Learning

no code implementations24 Aug 2023 Gege Qi, Yuefeng Chen, Xiaofeng Mao, Binyuan Hui, Xiaodan Li, Rong Zhang, Hui Xue

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice.

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

1 code implementation ICCV 2023 Xiaofeng Mao, Yuefeng Chen, Yao Zhu, Da Chen, Hang Su, Rong Zhang, Hui Xue

To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.

Autonomous Driving Object +2

Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

no code implementations24 Jul 2023 Gege Qi, Yuefeng Chen, Xiaofeng Mao, Xiaojun Jia, Ranjie Duan, Rong Zhang, Hui Xue

Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

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

1 code implementation29 May 2023 Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.

Adversarial Attack

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

no code implementations26 May 2023 Ruixiang Tang, Dehan Kong, Longtao Huang, Hui Xue

Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts).

In-Context Learning

Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection

no code implementations20 May 2023 Xiao-Min Zeng, Yan Song, Zhu Zhuo, Yu Zhou, Yu-Hong Li, Hui Xue, Li-Rong Dai, Ian McLoughlin

In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD).

Contrastive Learning Representation Learning

FairRec: Fairness Testing for Deep Recommender Systems

1 code implementation14 Apr 2023 Huizhong Guo, Jinfeng Li, Jingyi Wang, Xiangyu Liu, Dongxia Wang, Zehong Hu, Rong Zhang, Hui Xue

Given the testing report, by adopting a simple re-ranking mitigation strategy on these identified disadvantaged groups, we show that the fairness of DRSs can be significantly improved.

Fairness Recommendation Systems +1

ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing

2 code implementations CVPR 2023 Xiaodan Li, Yuefeng Chen, Yao Zhu, Shuhui Wang, Rong Zhang, Hui Xue

We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models.

Attribute Benchmarking +1

PIAT: Parameter Interpolation based Adversarial Training for Image Classification

no code implementations24 Mar 2023 Kun He, Xin Liu, Yichen Yang, Zhou Qin, Weigao Wen, Hui Xue, John E. Hopcroft

Besides, we suggest to use the Normalized Mean Square Error (NMSE) to further improve the robustness by aligning the clean and adversarial examples.

Classification Image Classification

To Make Yourself Invisible with Adversarial Semantic Contours

no code implementations1 Mar 2023 Yichi Zhang, Zijian Zhu, Hang Su, Jun Zhu, Shibao Zheng, Yuan He, Hui Xue

In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour.

Autonomous Driving Object +2

A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking

no code implementations28 Feb 2023 Chang Liu, Yinpeng Dong, Wenzhao Xiang, Xiao Yang, Hang Su, Jun Zhu, Yuefeng Chen, Yuan He, Hui Xue, Shibao Zheng

In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.

Adversarial Robustness Benchmarking +2

Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain

no code implementations28 Feb 2023 Chang Liu, Wenzhao Xiang, Yuan He, Hui Xue, Shibao Zheng, Hang Su

To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement.

Data Augmentation

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

1 code implementation21 Feb 2023 Shipeng Zhu, Zuoyan Zhao, Pengfei Fang, Hui Xue

Scene text image super-resolution (STISR) aims to simultaneously increase the resolution and legibility of the text images, and the resulting images will significantly affect the performance of downstream tasks.

Image Super-Resolution

Open-Vocabulary Object Detection With an Open Corpus

no code implementations ICCV 2023 Jiong Wang, Huiming Zhang, Haiwen Hong, Xuan Jin, Yuan He, Hui Xue, Zhou Zhao

For the classification task, we introduce an open corpus classifier by reconstructing original classifier with similar words in text space.

Object object-detection +1

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

no code implementations30 Nov 2022 Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Rongxin Jiang, Bolun Zheng, Yaowu Chen

Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.

Model Selection Out-of-Distribution Detection +1

Context-Aware Robust Fine-Tuning

no code implementations29 Nov 2022 Xiaofeng Mao, Yuefeng Chen, Xiaojun Jia, Rong Zhang, Hui Xue, Zhao Li

Contrastive Language-Image Pre-trained (CLIP) models have zero-shot ability of classifying an image belonging to "[CLASS]" by using similarity between the image and the prompt sentence "a [CONTEXT] of [CLASS]".

Domain Generalization Sentence

RoChBert: Towards Robust BERT Fine-tuning for Chinese

1 code implementation28 Oct 2022 Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

Despite of the superb performance on a wide range of tasks, pre-trained language models (e. g., BERT) have been proved vulnerable to adversarial texts.

Data Augmentation Language Modelling

Boosting Out-of-distribution Detection with Typical Features

no code implementations9 Oct 2022 Yao Zhu, Yuefeng Chen, Chuanlong Xie, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Bolun Zheng, Yaowu Chen

Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.

Out-of-Distribution Detection

Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition

no code implementations22 Apr 2022 Yunqing Hu, Xuan Jin, Yin Zhang, Haiwen Hong, Jingfeng Zhang, Feihu Yan, Yuan He, Hui Xue

Finally, we propose a weakly supervised object localization-based approach to extract multi-scale local features, to form a multi-view pipeline.

Weakly-Supervised Object Localization

D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention

no code implementations2 Mar 2022 Junyu Lin, Xiaofeng Mao, Yuefeng Chen, Lei Xu, Yuan He, Hui Xue

DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing.


Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition

no code implementations16 Feb 2022 Yimu Wang, Kun Yu, Yan Wang, Hui Xue

In this paper, to extract a better feature for advancing the performance, we propose a novel method, namely multi-view fusion transformer (MVFT) along with a novel attention mechanism.

Human Activity Recognition Time Series +1

Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains

2 code implementations ICLR 2022 Qilong Zhang, Xiaodan Li, Yuefeng Chen, Jingkuan Song, Lianli Gao, Yuan He, Hui Xue

Notably, our methods outperform state-of-the-art approaches by up to 7. 71\% (towards coarse-grained domains) and 25. 91\% (towards fine-grained domains) on average.

An Automated Question-Answering Framework Based on Evolution Algorithm

no code implementations26 Jan 2022 Sinan Tan, Hui Xue, Qiyu Ren, Huaping Liu, Jing Bai

Our framework is based on an innovative evolution algorithm, which is stable and suitable for multiple dataset scenario.

Question Answering

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

no code implementations21 Jul 2021 Haiwen Hong, Xuan Jin, Yin Zhang, Yunqing Hu, Jingfeng Zhang, Yuan He, Hui Xue

In multimodal tasks, we find that the importance of text and image modal information is different for different input cases, and for this motivation, we propose a high-performance and highly general Dual-Router Dynamic Framework (DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit.

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

no code implementations17 Jul 2021 Yunqing Hu, Xuan Jin, Yin Zhang, Haiwen Hong, Jingfeng Zhang, Yuan He, Hui Xue

We propose the recurrent attention multi-scale transformer (RAMS-Trans), which uses the transformer's self-attention to recursively learn discriminative region attention in a multi-scale manner.

Fine-Grained Image Classification Fine-Grained Image Recognition

Towards Robust Vision Transformer

2 code implementations CVPR 2022 Xiaofeng Mao, Gege Qi, Yuefeng Chen, Xiaodan Li, Ranjie Duan, Shaokai Ye, Yuan He, Hui Xue

By using and combining robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness.

Domain Generalization Image Classification +1

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

no code implementations CVPR 2021 Xiaodan Li, Jinfeng Li, Yuefeng Chen, Shaokai Ye, Yuan He, Shuhui Wang, Hang Su, Hui Xue

Comprehensive experiments show that the proposed attack achieves a high attack success rate with few queries against the image retrieval systems under the black-box setting.

Image Classification Image Retrieval +1

Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation

no code implementations23 Feb 2021 Jinfeng Li, Tianyu Du, Xiangyu Liu, Rong Zhang, Hui Xue, Shouling Ji

Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective - it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic - its key component, i. e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient - it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.

Cut out the annotator, keep the cutout: better segmentation with weak supervision

no code implementations ICLR 2021 Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, Christopher Re

We propose a framework that fuses limited label learning and weak supervision for segmentation tasks, enabling users to train high-performing segmentation CNNs with very few hand-labeled training points.

Data Augmentation Few-Shot Learning +4

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

no code implementations14 Dec 2020 Xuan Jin, Wei Su, Rong Zhang, Yuan He, Hui Xue

To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations.

object-detection Object Detection

Composite Adversarial Attacks

1 code implementation10 Dec 2020 Xiaofeng Mao, Yuefeng Chen, Shuhui Wang, Hang Su, Yuan He, Hui Xue

Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness.

Adversarial Attack Adversarial Robustness

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

1 code implementation14 Aug 2020 Hui Xue, Jessica Artico, Marianna Fontana, James C. Moon, Rhodri H. Davies, Peter Kellman

Conclusions: This study developed, validated and deployed a CNN solution for robust landmark detection in both long and short-axis CMR images for cine, LGE and T1 mapping sequences, with the accuracy comparable to the inter-operator variation.

Sharp Multiple Instance Learning for DeepFake Video Detection

no code implementations11 Aug 2020 Xiaodan Li, Yining Lang, Yuefeng Chen, Xiaofeng Mao, Yuan He, Shuhui Wang, Hui Xue, Quan Lu

A sharp MIL (S-MIL) is proposed which builds direct mapping from instance embeddings to bag prediction, rather than from instance embeddings to instance prediction and then to bag prediction in traditional MIL.

Face Swapping Multiple Instance Learning

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

no code implementations3 Jul 2020 Yusi Zhang, Chuanjie Liu, Angen Luo, Hui Xue, Xuan Shan, Yuxiang Luo, Yiqian Xia, Yuanchi Yan, Haidong Wang

The common framework is to train two encoding models based on neural embedding which learn the distributed representations of queries and documents separately and match them in the latent semantic space.

Graph Attention Retrieval

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

no code implementations9 Jun 2020 Xiaofeng Mao, Yuefeng Chen, Yuhong Li, Yuan He, Hui Xue

Different from previous single-target attack models, our model can conduct target-conditioned attacks by learning the relations of attack target and the semantics in image.

Computational Efficiency

Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

1 code implementation29 May 2020 Wenwu Ye, Jin Yao, Hui Xue, Yi Li

Localizing thoracic diseases on chest X-ray plays a critical role in clinical practices such as diagnosis and treatment planning.

Towards Face Encryption by Generating Adversarial Identity Masks

1 code implementation ICCV 2021 Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue

As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention.

Face Recognition

Deeper Insights into Weight Sharing in Neural Architecture Search

1 code implementation6 Jan 2020 Yuge Zhang, Zejun Lin, Junyang Jiang, Quanlu Zhang, Yujing Wang, Hui Xue, Chen Zhang, Yaming Yang

With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention.

Neural Architecture Search

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

no code implementations18 Dec 2019 Da Chen, Yong-Liang Yang, Zunlei Feng, Xiang Wu, Mingli Song, Wenbin Li, Yuan He, Hui Xue, Feng Mao

This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.

Decoder Few-Shot Image Classification +2

Learning To Characterize Adversarial Subspaces

no code implementations15 Nov 2019 Xiaofeng Mao, Yuefeng Chen, Yuhong Li, Yuan He, Hui Xue

To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of \textit{adversarial subspaces} where adversarial examples lie.

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

1 code implementation15 Nov 2019 Xiaodan Li, Yuefeng Chen, Yuan He, Hui Xue

Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible perturbations.

Adversarial Robustness

Self-supervised Adversarial Training

1 code implementation15 Nov 2019 Kejiang Chen, Hang Zhou, Yuefeng Chen, Xiaofeng Mao, Yuhong Li, Yuan He, Hui Xue, Weiming Zhang, Nenghai Yu

Recent work has demonstrated that neural networks are vulnerable to adversarial examples.

Self-Supervised Learning

Self-Supervised Learning For Few-Shot Image Classification

2 code implementations14 Nov 2019 Da Chen, Yuefeng Chen, Yuhong Li, Feng Mao, Yuan He, Hui Xue

In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.

Classification cross-domain few-shot learning +3

DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition

no code implementations10 Oct 2019 Xupeng Miao, Nezihe Merve Gürel, Wentao Zhang, Zhichao Han, Bo Li, Wei Min, Xi Rao, Hansheng Ren, Yinan Shan, Yingxia Shao, Yujie Wang, Fan Wu, Hui Xue, Yaming Yang, Zitao Zhang, Yang Zhao, Shuai Zhang, Yujing Wang, Bin Cui, Ce Zhang

Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.

Graph Neural Network

Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

no code implementations2 Jun 2019 Feng Mao, Xiang Wu, Hui Xue, Rong Zhang

However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased.

General Classification Graph Neural Network +2

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

1 code implementation18 Mar 2019 Xiaofeng Mao, Yuefeng Chen, Yuhong Li, Tao Xiong, Yuan He, Hui Xue

The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description.

Generative Adversarial Network

Deep Features Analysis with Attention Networks

no code implementations20 Jan 2019 Shipeng Xie, Da Chen, Rong Zhang, Hui Xue

Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc.

Classification General Classification +3

Ranking Responses Oriented to Conversational Relevance in Chat-bots

no code implementations COLING 2016 Bowen Wu, Baoxun Wang, Hui Xue

For automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only.


Logistic Boosting Regression for Label Distribution Learning

no code implementations CVPR 2016 Chao Xing, Xin Geng, Hui Xue

In order to learn this general model family, this paper uses a method called Logistic Boosting Regression (LogitBoost) which can be seen as an additive weighted function regression from the statistical viewpoint.

Age Estimation Facial Expression Recognition +3

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

no code implementations8 Nov 2015 Steven C. H. Hoi, Xiongwei Wu, Hantang Liu, Yue Wu, Huiqiong Wang, Hui Xue, Qiang Wu

In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.

Logo Recognition object-detection +1

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