87 citations · 467 across the 21 of their papers we have counts for
5 papers · 1 filter
Sill-Net: Feature Augmentation with Separated Illumination Representation
Haipeng Zhang, Zhong Cao, Ziang Yan +1
For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models.…
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
Ziang Yan, Yiwen Guo, Changshui Zhang
Unlike the white-box counterparts that are widely studied and readily accessible, adversarial examples in black-box settings are generally more Herculean on account of the difficul…
Deep Defense: Training DNNs with Improved Adversarial Robustness
Ziang Yan, Yiwen Guo, Changshui Zhang
Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical syst…
Zero-Shot Learning by Generating Pseudo Feature Representations
Jiang Lu, Jin Li, Ziang Yan +1
Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel classes without any training instances. In this paper we present a simple but high-performance ZSL approa…
Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm
Ziang Yan, Jian Liang, Weishen Pan +2
Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large sc…