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09/05/2023
OHQ: On-chip Hardware-aware Quantization
Quantization emerges as one of the most promising approaches for deployi...
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08/04/2023
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep ne...
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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...
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07/01/2023
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to...
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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...
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05/22/2023
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Physical world adversarial attack is a highly practical and threatening ...
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05/19/2023
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
Machine learning (ML) systems have achieved remarkable performance acros...
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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...
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03/25/2023
Towards Accurate Post-Training Quantization for Vision Transformer
Vision transformer emerges as a potential architecture for vision tasks....
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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...
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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...
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11/19/2022
Phonemic Adversarial Attack against Audio Recognition in Real World
Recently, adversarial attacks for audio recognition have attracted much ...
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09/28/2022
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Adversarial training has been demonstrated to be one of the most effecti...
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09/27/2022
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Transformer architecture has become the fundamental element of the wides...
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09/15/2022
Improving Robust Fairness via Balance Adversarial Training
Adversarial training (AT) methods are effective against adversarial atta...
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09/03/2022
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
Existing Binary Neural Networks (BNNs) mainly operate on local convoluti...
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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...
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07/12/2022
Frequency Domain Model Augmentation for Adversarial Attack
For black-box attacks, the gap between the substitute model and the vict...
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04/13/2022
Defensive Patches for Robust Recognition in the Physical World
To operate in real-world high-stakes environments, deep learning systems...
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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...
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03/11/2022
QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Recently, post-training quantization (PTQ) has driven much attention to ...
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03/10/2022
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack
Defense models against adversarial attacks have grown significantly, but...
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01/03/2022
Revisiting Open World Object Detection
Open World Object Detection (OWOD), simulating the real dynamic world wh...
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09/16/2021
Harnessing Perceptual Adversarial Patches for Crowd Counting
Crowd counting, which is significantly important for estimating the numb...
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09/11/2021
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...
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09/01/2021
Diverse Sample Generation: Pushing the Limit of Data-free Quantization
Recently, generative data-free quantization emerges as a practical appro...
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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...
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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...
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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...
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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...
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03/01/2021
Diversifying Sample Generation for Accurate Data-Free Quantization
Quantization has emerged as one of the most prevalent approaches to comp...
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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...
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01/24/2021
A Comprehensive Evaluation Framework for Deep Model Robustness
Deep neural networks (DNNs) have achieved remarkable performance across ...
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12/03/2020
Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization
There is now extensive evidence demonstrating that deep neural networks ...
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07/14/2020
Patch-wise Attack for Fooling Deep Neural Network
By adding human-imperceptible noise to clean images, the resultant adver...
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05/19/2020
Patch Attack for Automatic Check-out
Adversarial examples are inputs with imperceptible perturbations that ea...
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05/19/2020
Adversarial Attacks for Embodied Agents
Adversarial attacks are valuable for providing insights into the blind-s...
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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...
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03/31/2020
Binary Neural Networks: A Survey
The binary neural network, largely saving the storage and computation, s...
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03/01/2020
Stein Variational Inference for Discrete Distributions
Gradient-based approximate inference methods, such as Stein variational ...
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02/17/2020
Hierarchical Rule Induction Network for Abstract Visual Reasoning
Abstract reasoning refers to the ability to analyze information, discove...
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02/01/2020
Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction
Extracting graph representation of visual scenes in image is a challengi...
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12/29/2019
Towards Unified INT8 Training for Convolutional Neural Network
Recently low-bit (e.g., 8-bit) network quantization has been extensively...
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11/25/2019
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Visual loop closure detection, which can be considered as an image retri...
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09/26/2019
Balanced Binary Neural Networks with Gated Residual
Binary neural networks have attracted numerous attention in recent years...
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09/25/2019
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Fine-grained visual categorization (FGVC) is an important but challengin...
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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...
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09/19/2019
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Deep neural networks have been found vulnerable to noises like adversari...
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09/16/2019
Interpreting and Improving Adversarial Robustness with Neuron Sensitivity
Deep neural networks (DNNs) are vulnerable to adversarial examples where...
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09/11/2019
