Class-Aware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models

• URL: http://arxiv.org/abs/2510.19802v1
• Date: Wed, 22 Oct 2025 17:38:35 GMT
• Title: Class-Aware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models
• Authors: Xiaozhen Qiao, Jingkai Zhao, Yuqiu Jiang, Xianda Guo, Zhe Sun, Hongyuan Zhang, Xuelong Li,
• Abstract summary: Vision-Language Models (VLMs) demonstrate impressive zero-shot generalization through large-scale image-text pretraining.<n>But their performance can drop once the deployment distribution diverges from the training distribution.<n>Test-Time Adaptation (TTA) methods update models using unlabeled target data.<n>We propose textbfClass-Aware textbfPrototype textbfL with textbfNegative textbfContrast(textbfCPL-NC), a lightweight TTA framework
• Score: 48.61795272482598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) demonstrate impressive zero-shot generalization through large-scale image-text pretraining, yet their performance can drop once the deployment distribution diverges from the training distribution. To address this, Test-Time Adaptation (TTA) methods update models using unlabeled target data. However, existing approaches often ignore two key challenges: prototype degradation in long-tailed distributions and confusion between semantically similar classes. To tackle these issues, we propose \textbf{C}lass-Aware \textbf{P}rototype \textbf{L}earning with \textbf{N}egative \textbf{C}ontrast(\textbf{CPL-NC}), a lightweight TTA framework designed specifically for VLMs to enhance generalization under distribution shifts. CPL-NC introduces a \textit{Class-Aware Prototype Cache} Module that dynamically adjusts per-class capacity based on test-time frequency and activation history, with a rejuvenation mechanism for inactive classes to retain rare-category knowledge. Additionally, a \textit{Negative Contrastive Learning} Mechanism identifies and constrains hard visual-textual negatives to improve class separability. The framework employs asymmetric optimization, refining only textual prototypes while anchoring on stable visual features. Experiments on 15 benchmarks show that CPL-NC consistently outperforms prior TTA methods across both ResNet-50 and ViT-B/16 backbones.

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