标题： 小帮助，大飞跃：使用 AdaptNet 的视觉-语言模型高效测试时自适应 显示英文标题

标题： Small Aid, Big Leap: Efficient Test-Time Adaptation for Vision-Language Models with AdaptNet

摘要： 测试时适应（TTA）已成为提高视觉-语言模型（VLMs）推理阶段泛化能力的关键技术。然而，现有的方法通常伴随着显著的计算开销，并且扩展性较差，主要原因是样本级别的适应粒度以及对昂贵辅助设计（如数据增强）的依赖。为了解决这些局限性，我们提出了SAIL（Small Aid, Big Leap），这是一种基于适配器的新颖TTA框架，利用轻量级可学习的AdaptNet实现高效且可扩展的模型适应。作为SAIL的核心，一个冻结的预训练VLM通过基于置信度的插值权重与AdaptNet协作，在推理过程中生成鲁棒预测。这些预测用作自监督目标，通过高效的批量处理来对齐AdaptNet的输出，从而大幅降低计算成本，而无需修改VLM或需要内存缓存。为了减轻连续适应过程中的灾难性遗忘，我们提出了一种基于梯度漂移指示器（GDI）的梯度感知重置策略，该策略能够动态检测领域转换并战略性地重置AdaptNet以确保稳定适应。在两种场景下的多个基准测试中进行了广泛的实验，结果显示SAIL达到了最先进的性能，同时保持了较低的计算成本。这些结果凸显了SAIL在实际部署中的有效性、效率和可扩展性。代码将在接受后发布。 显示英文摘要

摘要： Test-time adaptation (TTA) has emerged as a critical technique for enhancing the generalization capability of vision-language models (VLMs) during inference. However, existing approaches often incur substantial computational costs and exhibit poor scalability, primarily due to sample-wise adaptation granularity and reliance on costly auxiliary designs such as data augmentation. To address these limitations, we introduce SAIL (Small Aid, Big Leap), a novel adapter-based TTA framework that leverages a lightweight, learnable AdaptNet to enable efficient and scalable model adaptation. As SAIL's core, a frozen pre-trained VLM collaborates with AdaptNet through a confidence-based interpolation weight, generating robust predictions during inference. These predictions serve as self-supervised targets to align AdaptNet's outputs through efficient batch-wise processing, dramatically reducing computational costs without modifying the VLM or requiring memory caches. To mitigate catastrophic forgetting during continual adaptation, we propose a gradient-aware reset strategy driven by a gradient drift indicator (GDI), which dynamically detects domain transitions and strategically resets AdaptNet for stable adaptation. Extensive experiments across diverse benchmarks on two scenarios demonstrate that SAIL achieves state-of-the-art performance while maintaining low computational costs. These results highlight SAIL's effectiveness, efficiency and scalability for real-world deployment. The code will be released upon acceptance.