Pre-trained 3D vision-language models have demonstrated strong semantic generalization and robustness to distribution shifts. However, the implications of semantic robustness for geometric stability remain unclear. This study revisits the adversarial robustness of 3D vision-language models when confronted with adversarial point clouds. Focusing on zero-shot classification, this study demonstrates that these models exhibit heightened sensitivity to small coordinate perturbations. The behavior of adversarial perturbations is further analyzed under widely used point-cloud preprocessing mechanisms, revealing that naive filtering or reconstruction mainly suppresses irregular perturbations produced by vanilla gradient-based attacks and provides limited protection against stronger attack methods. To this end, a refined adversarial objective is introduced with two complementary priors that encourage adversarial point clouds to remain smooth and geometrically plausible: a statistical prior that regularizes the sampling distribution, and a geometric prior that promotes consistency with a plausible object-surface manifold. These findings highlight the need for a more rigorous security evaluation of 3D vision-language models.
Xuanxiang Lin, Yan Huang, Longkun Zou et al.· IEEE Access· 0 citations
Vision-language models (VLMs) such as CLIP exhibit remarkable zero-shot capabilities, yet their performance frequently degrades sharply under unexpected test-time distribution shifts. While Test-Time Adaptation (TTA) offers a promising solution, continuously adapting VLMs over an unlabeled test stream presents fundamental challenges. Conventional top-1-centric updates often reinforce errors by corrupting the local semantic geometry among related classes, while iterative adaptation exacerbates progressive bias accumulation, ultimately driving the model toward mode collapse. To overcome these coupled vulnerabilities, we propose Local Margin Restoration (LMR), a lightweight, one-step TTA framework. At the sample level, our Protected Margin Restoration (PMR) objective recovers local semantic geometry by shielding plausible near-top candidates from external hard negatives. Concurrently, to combat stream-level degradation, we introduce a dual-stage stabilization mechanism, featuring an Adaptive Margin (AM) controller and Bias Correction (BC), to dynamically disrupt progressive bias accumulation and prevent mode collapse. Extensive experiments on CIFAR-C, ImageNet-C, and ImageNet variants demonstrate that LMR consistently outperforms state-of-the-art TTA baselines, proving exceptionally robust and efficient even in challenging low-batch test-time regimes. Our code is available at https://github.com/DennisHuangYan/LMR.
Yan Huang, Guowei Wang, Xu Wang et al.· 0 citations