ANPrompt: Anti-noise Prompt Tuning for Vision-Language Models

• URL: http://arxiv.org/abs/2508.04677v2
• Date: Thu, 07 Aug 2025 11:50:06 GMT
• Title: ANPrompt: Anti-noise Prompt Tuning for Vision-Language Models
• Authors: Yansheng Gao, Yufei Zheng, Jinghan Qu, Zixi Zhu, Yukuan Zhang, Shengsheng Wang,
• Abstract summary: We propose ANPrompt, a novel prompt tuning framework to enhance robustness under noise perturbations.<n>ANPrompt constructs weak noise text features by fusing original and noise-perturbed text embeddings, which are then clustered to form noise prompts.<n>Experiments across 11 benchmarks demonstrate that ANPrompt consistently outperforms existing prompt tuning approaches.
• Score: 0.5717569761927883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompt tuning has emerged as an efficient and effective technique for adapting vision-language models (VLMs) with low computational overhead. However, existing methods often overlook the vulnerability of prompt-tuned VLMs to weak semantic perturbations-such as subtle image or text noise-that degrade their generalization to unseen classes. To address this limitation, we propose ANPrompt, a novel prompt tuning framework designed to enhance robustness under such perturbations. ANPrompt first constructs weak noise text features by fusing original and noise-perturbed text embeddings, which are then clustered to form noise prompts. These noise prompts are integrated with learnable prompt tokens to generate anti-noise prompts, which are injected into the deeper layers of both image and text encoders. To further capture the noise-aware visual semantics, ANPrompt computes the Noise-Resistant Visual Prompt Prototype (NRVPP) by averaging the output prompt tokens from the vision encoder. Finally, ANPrompt introduces alignment, robustness, and anti-noise objectives by computing a Weak semantic noise Alignment Loss (WALoss) alongside the standard cross-entropy and sim loss. Experiments across 11 benchmarks demonstrate that ANPrompt consistently outperforms existing prompt tuning approaches, achieving superior robustness to semantic noise and improved generalization to novel categories.

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