DESA-TTA：用于测试时适应的动态指数移动平均与源锚定

中文摘要 AI 辅助

视觉-语言目标检测器（VLOD）实现了出色的零样本性能，但在部署过程中仍易受分布偏移影响。用于测试时适应（TTA）的均值教师方法可通过使用教师生成的伪标签更新学生模型来提升鲁棒性，但均值教师TTA对教师更新所用的固定指数移动平均（EMA）系数选择高度敏感，且用含噪伪标签反复优化会导致学生模型发生累积漂移。本文提出用于TTA的低开销方法DESA-TTA，该方法通过动态时间平均与源锚定共同调控教师更新和学生漂移：动态时间平均从伪标签置信度与框密度中估计教师不确定性，并用其在教师参数漂移确定的边界内逐样本选择EMA系数；源锚定则将更新后的学生参数部分恢复至预训练值，且锚定强度随学生漂移增大。在多种分布偏移及两种VLOD架构上的实验显示，DESA-TTA相较现有TTA方法取得了一致提升；在VOC-C数据集上，DESA-TTA较零样本推理将AP₅₀提升14.5个百分点，同时比YOLO-World的现有最优TTA方法实现55%更高的推理吞吐量。我们的代码：this https URL

英文摘要

Vision-language object detectors (VLODs) achieve strong zero-shot performance but remain vulnerable to distribution shifts during deployment. Mean-teacher methods for test-time adaptation (TTA) can improve robustness by updating a student model using teacher-generated pseudo-labels. However, mean-teacher TTA is highly sensitive to the choice of a fixed exponential moving average (EMA) coefficient for teacher updates, and repeated optimization with noisy pseudo-labels can cause cumulative student drift. We propose Dynamic EMA and Source Anchoring for TTA (DESA-TTA), a low-overhead method that jointly regulates teacher updates and student drift through dynamic temporal averaging and source anchoring. Dynamic temporal averaging estimates teacher uncertainty from pseudo-label confidence and box density and uses it to select a sample-wise EMA coefficient within bounds determined by teacher parameter drift. Source anchoring partially restores the updated student parameters toward their pretrained values, with the anchoring strength increasing according to student drift. Experiments across diverse distribution shifts and two VLOD architectures show consistent improvements over existing TTA methods. On VOC-C, DESA-TTA improves AP$_{50}$ by 14.5 points over zero-shot inference while achieving 55\% higher inference throughput than the previous state-of-the-art TTA method for YOLO-World. Our code: https://github.com/imatif17/DESA-TTA

发表机构

• LIVIA, ILLS, Dept. of Systems Engineering, ETS Montréal（LIVIA、ILLS、蒙特利尔理工大学系统工程系）

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