Search Results for author: Rong Zhang

Found 37 papers, 15 papers with code

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 implementation24 Jul 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-detection +1

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

CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility

1 code implementation19 Jul 2023 Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, Ji Zhang, Chao Peng, Fei Huang, Jingren Zhou

In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.

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

1 code implementation 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.


Information-containing Adversarial Perturbation for Combating Facial Manipulation Systems

no code implementations21 Mar 2023 Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Xiang Tian, Bolun Zheng, Yaowu Chen

We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.

Fake Image Detection

PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development

1 code implementation23 Jan 2023 Avirup Sil, Jaydeep Sen, Bhavani Iyer, Martin Franz, Kshitij Fadnis, Mihaela Bornea, Sara Rosenthal, Scott McCarley, Rong Zhang, Vishwajeet Kumar, Yulong Li, Md Arafat Sultan, Riyaz Bhat, Radu Florian, Salim Roukos

The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.

Question Answering Reading Comprehension +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

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

1 code implementation9 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 Out of Distribution (OOD) Detection

PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification

no code implementations11 Jul 2022 Kanghao Chen, Weixian Lei, Rong Zhang, Shen Zhao, Wei-Shi Zheng, Ruixuan Wang

For the class-center involved triplet loss, the positive and negative samples in each triplet are replaced by their corresponding class centers, which enforces data representations of the same class closer to the class center.

Image Classification Medical Image Classification

Graph Pattern Loss based Diversified Attention Network for Cross-Modal Retrieval

no code implementations25 Jun 2021 Xueying Chen, Rong Zhang, Yibing Zhan

In this paper, we propose a Graph Pattern Loss based Diversified Attention Network(GPLDAN) for unsupervised cross-modal retrieval to deeply analyze correlations among representations.

Cross-Modal Retrieval Retrieval

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.

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

End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

no code implementations2 Dec 2020 Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan, Rong Zhang, Avi Sil, Vittorio Castelli, Radu Florian, Salim Roukos

End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.

Domain Adaptation Information Retrieval +3

Enhancing Neural Models with Vulnerability via Adversarial Attack

1 code implementation COLING 2020 Rong Zhang, Qifei Zhou, Bo An, Weiping Li, Tong Mo, Bo Wu

2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.

Adversarial Attack

AutoRemover: Automatic Object Removal for Autonomous Driving Videos

1 code implementation28 Nov 2019 Rong Zhang, Wei Li, Peng Wang, Chenye Guan, Jin Fang, Yuhang Song, Jinhui Yu, Baoquan Chen, Weiwei Xu, Ruigang Yang

To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically.

Autonomous Driving Video Inpainting

Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms

no code implementations7 Sep 2019 Meng Xia, Huan Wei, Min Xu, Leo Yu Ho Lo, Yong Wang, Rong Zhang, Huamin Qu

With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded.

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 Video Classification +1

AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms

1 code implementation23 Jan 2019 Wei Li, Chengwei Pan, Rong Zhang, Jiaping Ren, Yuexin Ma, Jin Fang, Feilong Yan, Qichuan Geng, Xinyu Huang, Huajun Gong, Weiwei Xu, Guoping Wang, Dinesh Manocha, Ruigang Yang

Our augmented approach combines the flexibility in a virtual environment (e. g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.

Autonomous Driving

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

Review of Deep Learning

no code implementations5 Apr 2018 Rong Zhang, Weiping Li, Tong Mo

In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence.

An influence-based fast preceding questionnaire model for elderly assessments

no code implementations22 Nov 2017 Tong Mo, Rong Zhang, Weiping Li, Jingbo Zhang, Zhonghai Wu, Wei Tan

The practice in an elderly-care company shows that the FPQM can reduce the number of attributes by 90. 56% with a prediction accuracy of 98. 39%.

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