b'Xianglong Liu'

research

∙ 09/05/2023

OHQ: On-chip Hardware-aware Quantization

Quantization emerges as one of the most promising approaches for deployi...

0 Wei Huang, et al. ∙

research

∙ 08/04/2023

RobustMQ: Benchmarking Robustness of Quantized Models

Quantization has emerged as an essential technique for deploying deep ne...

0 Yisong Xiao, et al. ∙

research

∙ 08/02/2023

Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks

Despite the broad application of Machine Learning models as a Service (M...

0 Jun Guo, et al. ∙

research

∙ 07/01/2023

SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency

Extensive studies have shown that deep learning models are vulnerable to...

0 Yan Wang, et al. ∙

research

∙ 05/28/2023

One stone, two birds: A lightweight multidimensional learned index with cardinality support

Innovative learning based structures have recently been proposed to tack...

0 Yingze Li, et al. ∙

research

∙ 05/22/2023

Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks

Physical world adversarial attack is a highly practical and threatening ...

0 Simin Li, et al. ∙

research

∙ 05/19/2023

Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing

Machine learning (ML) systems have achieved remarkable performance acros...

0 Yisong Xiao, et al. ∙

research

∙ 04/18/2023

Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling

Quantization of transformer language models faces significant challenges...

0 Xiuying Wei, et al. ∙

research

∙ 03/25/2023

Towards Accurate Post-Training Quantization for Vision Transformer

Vision transformer emerges as a potential architecture for vision tasks....

0 Yifu Ding, et al. ∙

research

∙ 02/25/2023

TS-Cabinet: Hierarchical Storage for Cloud-Edge-End Time-series Database

Hierarchical data storage is crucial for cloud-edge-end time-series data...

0 Shuangshuang Cui, et al. ∙

research

∙ 02/19/2023

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

Adversarial attacks are valuable for evaluating the robustness of deep l...

0 Aishan Liu, et al. ∙

research

∙ 11/19/2022

Phonemic Adversarial Attack against Audio Recognition in Real World

Recently, adversarial attacks for audio recognition have attracted much ...

0 Jiakai Wang, et al. ∙

research

∙ 09/28/2022

Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effecti...

0 Shiyu Tang, et al. ∙

research

∙ 09/27/2022

Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models

Transformer architecture has become the fundamental element of the wides...

0 Xiuying Wei, et al. ∙

research

∙ 09/15/2022

Improving Robust Fairness via Balance Adversarial Training

Adversarial training (AT) methods are effective against adversarial atta...

0 Chunyu Sun, et al. ∙

research

∙ 09/03/2022

Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies

Existing Binary Neural Networks (BNNs) mainly operate on local convoluti...

0 Xingrun Xing, et al. ∙

research

∙ 08/23/2022

Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection

Billions of people are sharing their daily life images on social media e...

0 Simin Li, et al. ∙

research

∙ 07/12/2022

Frequency Domain Model Augmentation for Adversarial Attack

For black-box attacks, the gap between the substitute model and the vict...

16 Yuyang Long, et al. ∙

research

∙ 04/13/2022

Defensive Patches for Robust Recognition in the Physical World

To operate in real-world high-stakes environments, deep learning systems...

0 Jiakai Wang, et al. ∙

research

∙ 03/21/2022

Delving into the Estimation Shift of Batch Normalization in a Network

Batch normalization (BN) is a milestone technique in deep learning. It n...

0 Lei Huang, et al. ∙

research

∙ 03/11/2022

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Recently, post-training quantization (PTQ) has driven much attention to ...

0 Xiuying Wei, et al. ∙

research

∙ 03/10/2022

Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack

Defense models against adversarial attacks have grown significantly, but...

0 Ye Liu, et al. ∙

research

∙ 01/03/2022

Revisiting Open World Object Detection

Open World Object Detection (OWOD), simulating the real dynamic world wh...

107 Xiaowei Zhao, et al. ∙

research

∙ 09/16/2021

Harnessing Perceptual Adversarial Patches for Crowd Counting

Crowd counting, which is significantly important for estimating the numb...

5 Shunchang Liu, et al. ∙

research

∙ 09/11/2021

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...

4 Shiyu Tang, et al. ∙

research

∙ 09/01/2021

Diverse Sample Generation: Pushing the Limit of Data-free Quantization

Recently, generative data-free quantization emerges as a practical appro...

4 Haotong Qin, et al. ∙

research

∙ 08/23/2021

Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection

Prohibited items detection in X-ray images often plays an important role...

0 Renshuai Tao, et al. ∙

research

∙ 08/16/2021

Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark

Automatic security inspection using computer vision technology is a chal...

8 Boying Wang, et al. ∙

research

∙ 03/10/2021

Multi-Pretext Attention Network for Few-shot Learning with Self-supervision

Few-shot learning is an interesting and challenging study, which enables...

13 Hainan Li, et al. ∙

research

∙ 03/01/2021

Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World

Deep learning models are vulnerable to adversarial examples. As a more t...

0 Jiakai Wang, et al. ∙

research

∙ 03/01/2021

Diversifying Sample Generation for Accurate Data-Free Quantization

Quantization has emerged as one of the most prevalent approaches to comp...

0 Xiangguo Zhang, et al. ∙

research

∙ 03/01/2021

Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images

Security inspection is X-ray scanning for personal belongings in suitcas...

0 Renshuai Tao, et al. ∙

research

∙ 01/24/2021

A Comprehensive Evaluation Framework for Deep Model Robustness

Deep neural networks (DNNs) have achieved remarkable performance across ...

0 Aishan Liu, et al. ∙

research

∙ 12/03/2020

Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization

There is now extensive evidence demonstrating that deep neural networks ...

0 Aishan Liu, et al. ∙

research

∙ 07/14/2020

Patch-wise Attack for Fooling Deep Neural Network

By adding human-imperceptible noise to clean images, the resultant adver...

12 Lianli Gao, et al. ∙

research

∙ 05/19/2020

Patch Attack for Automatic Check-out

Adversarial examples are inputs with imperceptible perturbations that ea...

5 Aishan Liu, et al. ∙

research

∙ 05/19/2020

Adversarial Attacks for Embodied Agents

Adversarial attacks are valuable for providing insights into the blind-s...

40 Aishan Liu, et al. ∙

research

∙ 04/18/2020

Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module

Object detection has taken advantage of the advances in deep convolution...

8 Yanlu Wei, et al. ∙

research

∙ 03/31/2020

Binary Neural Networks: A Survey

The binary neural network, largely saving the storage and computation, s...

22 Haotong Qin, et al. ∙

research

∙ 03/01/2020

Stein Variational Inference for Discrete Distributions

Gradient-based approximate inference methods, such as Stein variational ...

15 Jun Han, et al. ∙

research

∙ 02/17/2020

Hierarchical Rule Induction Network for Abstract Visual Reasoning

Abstract reasoning refers to the ability to analyze information, discove...

14 Sheng Hu, et al. ∙

research

∙ 02/01/2020

Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction

Extracting graph representation of visual scenes in image is a challengi...

11 Bin Wen, et al. ∙

research

∙ 12/29/2019

Towards Unified INT8 Training for Convolutional Neural Network

Recently low-bit (e.g., 8-bit) network quantization has been extensively...

21 Feng Zhu, et al. ∙

research

∙ 11/25/2019

Fast and Incremental Loop Closure Detection Using Proximity Graphs

Visual loop closure detection, which can be considered as an image retri...

13 Shan An, et al. ∙

research

∙ 09/26/2019

Balanced Binary Neural Networks with Gated Residual

Binary neural networks have attracted numerous attention in recent years...

5 Mingzhu Shen, et al. ∙

research

∙ 09/25/2019

Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) is an important but challengin...

17 Ruyi Ji, et al. ∙

research

∙ 09/24/2019

IR-Net: Forward and Backward Information Retention for Highly Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neur...

1 Haotong Qin, et al. ∙

research

∙ 09/19/2019

Training Robust Deep Neural Networks via Adversarial Noise Propagation

Deep neural networks have been found vulnerable to noises like adversari...

0 Aishan Liu, et al. ∙

research

∙ 09/16/2019

Interpreting and Improving Adversarial Robustness with Neuron Sensitivity

Deep neural networks (DNNs) are vulnerable to adversarial examples where...

14 Chongzhi Zhang, et al. ∙

research

∙ 09/11/2019

Towards Noise-Robust Neural Networks via Progressive Adversarial Training

Adversarial examples, intentionally designed inputs tending to mislead d...

0 Hang Yu, et al. ∙

Xianglong Liu

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