111 citations · 113 across the 5 of their papers we have counts for
5 papers
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models
Peng-Fei Zhang, Guangdong Bai, Zi Huang
Current adversarial attacks for evaluating the robustness of vision-language pre-trained (VLP) models in multi-modal tasks suffer from limited transferability, where attacks crafte…
Universal Adversarial Perturbations for Vision-Language Pre-trained Models
Peng-Fei Zhang, Zi Huang, Guangdong Bai
Vision-language pre-trained (VLP) models have been the foundation of numerous vision-language tasks. Given their prevalence, it becomes imperative to assess their adversarial robus…
Effective and Robust Adversarial Training against Data and Label Corruptions
Peng-Fei Zhang, Zi Huang, Xin-Shun Xu +1
Corruptions due to data perturbations and label noise are prevalent in the datasets from unreliable sources, which poses significant threats to model training. Despite existing eff…
Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation
Zixin Wang, Yadan Luo, Peng-Fei Zhang +2
A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior…
Lightweight Self-Attentive Sequential Recommendation
Yang Li, Tong Chen, Peng-Fei Zhang +1
Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various se…