Search Results for author:
Found 7 papers, 4 papers with code
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
Vision-Language Pre-training (VLP) models like CLIP have achieved remarkable success in computer vision and particularly demonstrated superior robustness to distribution shifts of 2D images.
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
However, the field of transferable targeted 3D adversarial attacks remains vacant.
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training
Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object.
Distributional Modeling for Location-Aware Adversarial Patches
In this paper, we propose the Distribution-Optimized Adversarial Patch (DOPatch), a novel method that optimizes a multimodal distribution of adversarial locations instead of individual ones.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
In this paper, we propose DIFFender, a novel defense method that leverages a text-guided diffusion model to defend against adversarial patches.
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift.
