85 citations · 101 across the 8 of their papers we have counts for
8 papers
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…
Single Image Super-Resolution via a Holistic Attention Network
Ben Niu, Weilei Wen, Wenqi Ren +6
Informative features play a crucial role in the single image super-resolution task. Channel attention has been demonstrated to be effective for preserving information-rich features…
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…
Face Super-Resolution Guided by 3D Facial Priors
Xiaobin Hu, Wenqi Ren, John LaMaster +5
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local app…
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma, Jinshan Zeng, Qianqian Xu +3
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi…
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI
Qianqian Xu, Xinwei Sun, Zhiyong Yang +3
Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. Howeve…