activity
20182023
most citedFormer: Calibrated and Complementary Transformer for RGB-Infrared Object Detection

187 citations · 293 across the 33 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

cs.CV2020★ 5 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.CV2020★ 23 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.…

cs.CR2020★ 8 cited

Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples

Xiaojun Jia, Xingxing Wei, Xiaochun Cao +1

Recent research has demonstrated that adding some imperceptible perturbations to original images can fool deep learning models. However, the current adversarial perturbations are u…

cs.CV2020★ 3 cited

Efficient Adversarial Attacks for Visual Object Tracking

Siyuan Liang, Xingxing Wei, Siyuan Yao +1

Visual object tracking is an important task that requires the tracker to find the objects quickly and accurately. The existing state-ofthe-art object trackers, i.e., Siamese based…

cs.SI2020

Attention: to Better Stand on the Shoulders of Giants

Sha Yuan, Zhou Shao, Yu Zhang +4

Science of science (SciSci) is an emerging discipline wherein science is used to study the structure and evolution of science itself using large data sets. The increasing availabil…

cs.CV2020

Sparse Black-box Video Attack with Reinforcement Learning

Xingxing Wei, Huanqian Yan, Bo Li

Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To…