6 citations · 15 across the 11 of their papers we have counts for
11 papers
From Semantics to Pixels: Coarse-to-Fine Masked Autoencoders for Hierarchical Visual Understanding
Wenzhao Xiang, Yue Wu, Hongyang Yu +3
Self-supervised visual pre-training methods face an inherent tension: contrastive learning (CL) captures global semantics but loses fine-grained detail, while masked image modeling…
Wavelet-Driven Masked Image Modeling: A Path to Efficient Visual Representation
Wenzhao Xiang, Chang Liu, Hongyang Yu +1
Masked Image Modeling (MIM) has garnered significant attention in self-supervised learning, thanks to its impressive capacity to learn scalable visual representations tailored for…
AEMIM: Adversarial Examples Meet Masked Image Modeling
Wenzhao Xiang, Chang Liu, Hang Su +1
Masked image modeling (MIM) has gained significant traction for its remarkable prowess in representation learning. As an alternative to the traditional approach, the reconstruction…
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
Chang Liu, Yinpeng Dong, Wenzhao Xiang +7
The robustness of deep neural networks is usually lacking under adversarial examples, common corruptions, and distribution shifts, which becomes an important research problem in th…
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
Chang Liu, Wenzhao Xiang, Yuan He +3
Deep neural networks (DNNs) may suffer from significantly degenerated performance when the training and test data are of different underlying distributions. Despite the importance…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness
Xiao Yang, Yinpeng Dong, Wenzhao Xiang +3
The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness. However, the progress is…
You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors
Zijian Zhu, Hang Su, Chang Liu +2
Blind spots or outright deceit can bedevil and deceive machine learning models. Unidentified objects such as digital "stickers," also known as adversarial patches, can fool facial…
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator
Wenzhao Xiang, Hang Su, Chang Liu +2
As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities. Designed and trained from certain statistical distri…
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE
Wenzhao Xiang, Chang Liu, Shibao Zheng
Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, un-restricted adversarial attack h…