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20182022
most citedObject Hider: Adversarial Patch Attack Against Object Detectors

23 citations · 81 across the 11 of their papers we have counts for

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11 papers · 1 filter

cs.CV202218 cited

ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints

Yinpeng Dong, Shouwei Ruan, Hang Su +3

Recent studies have demonstrated that visual recognition models lack robustness to distribution shift. However, current work mainly considers model robustness to 2D image transform…

cs.CV20224 cited

Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection

Maoxun Yuan, Yinyan Wang, Xingxing Wei

Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) imag…

cs.CV20222 cited

Parallel Rectangle Flip Attack: A Query-based Black-box Attack against Object Detection

Siyuan Liang, Baoyuan Wu, Yanbo Fan +2

Object detection has been widely used in many safety-critical tasks, such as autonomous driving. However, its vulnerability to adversarial examples has not been sufficiently studie…

cs.CV20217 cited

An Effective and Robust Detector for Logo Detection

Xiaojun Jia, Huanqian Yan, Yonglin Wu +3

In recent years, intellectual property (IP), which represents literary, inventions, artistic works, etc, gradually attract more and more people's attention. Particularly, with the…

cs.CV20205 cited

Automated Model Compression by Jointly Applied Pruning and Quantization

Wenting Tang, Xingxing Wei, Bo Li

In the traditional deep compression framework, iteratively performing network pruning and quantization can reduce the model size and computation cost to meet the deployment require…

cs.CV202023 cited

Object Hider: Adversarial Patch Attack Against Object Detectors

Yusheng Zhao, Huanqian Yan, Xingxing Wei

Deep neural networks have been widely used in many computer vision tasks. However, it is proved that they are susceptible to small, imperceptible perturbations added to the input.…