1 citations · 2 across the 4 of their papers we have counts for
5 papers
Consistency Training Along the Transformer Stack
Sukrati Gautam, Neil Shah, Arav Dhoot +7
Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the scope of consistency training…
Hierarchical Refinement of Universal Multimodal Attacks on Vision-Language Models
Peng-Fei Zhang, Zi Huang
Existing adversarial attacks for VLP models are mostly sample-specific, resulting in substantial computational overhead when scaled to large datasets or new scenarios. To overcome…
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…
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
Danny Wang, Ruihong Qiu, Guangdong Bai +1
Despite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One…
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…