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20192024
most citedQuery2Label: A Simple Transformer Way to Multi-Label Classification

121 citations · 153 across the 10 of their papers we have counts for

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8 papers · 1 filter

cs.CV2024

Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches

Lingxuan Wu, Xiao Yang, Yinpeng Dong +3

The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness. However, the prevailing defenses depend on…

cs.CV2023

Towards Transferable Targeted 3D Adversarial Attack in the Physical World

Yao Huang, Yinpeng Dong, Shouwei Ruan +3

Compared with transferable untargeted attacks, transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples, posing a greater thr…

cs.CV20216 cited

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…

cs.CV20211 cited

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.…

cs.CV2021

Adversarial Semantic Contour for Object Detection

Yichi Zhang, Zijian Zhu, Xiao Yang +1

Modern object detectors are vulnerable to adversarial examples, which brings potential risks to numerous applications, e.g., self-driving car. Among attacks regularized by

cs.CV2021121 cited

Query2Label: A Simple Transformer Way to Multi-Label Classification

Shilong Liu, Lei Zhang, Xiao Yang +2

This paper presents a simple and effective approach to solving the multi-label classification problem. The proposed approach leverages Transformer decoders to query the existence o…