For real-world applications, neural network models are commonly deployed in dynamic environments, where the distribution of the target domain undergoes temporal changes. Continual Test-Time Adaptation (CTTA) has recently emerged as a promising technique to gradually adapt a source-trained model to test data drawn from a continually changing target domain. Despite recent advancements in addressing CTTA, two critical issues remain: 1) The use of a fixed threshold for pseudo-labeling in existing methodologies leads to the generation of low-quality pseudo-labels, as model confidence varies across categories and domains; 2) While current solutions utilize stochastic parameter restoration to mitigate catastrophic forgetting, their capacity to preserve critical information is undermined by its intrinsic randomness. To tackle these challenges, we present CTAOD, aiming to enhance the performance of detection models in CTTA scenarios. Inspired by prior CTTA works for effective adaptation, CTAOD is founded on the mean-teacher framework, characterized by three core components. Firstly, the object-level contrastive learning module tailored for object detection extracts object-level features using the teacher's region of interest features and optimizes them through contrastive learning. Secondly, the dynamic threshold strategy updates the category-specific threshold based on predicted confidence scores to improve the quality of pseudo-labels. Lastly, we design a data-driven stochastic restoration mechanism to selectively reset inactive parameters using the gradients as weights for a random mask matrix, thereby ensuring the retention of essential knowledge. We demonstrate the effectiveness of our approach on four CTTA tasks for object detection, where CTAOD outperforms existing methods, especially achieving a 3.0 mAP improvement on the Cityscapes-to-Cityscapes-C CTTA task.

翻译：在现实应用中，神经网络模型通常部署在动态环境中，其中目标域的分布会随时间发生变化。持续测试时自适应（CTTA）作为一种新兴技术，旨在将源域训练的模型逐步适应于从持续变化的目标域中抽取的测试数据。尽管CTTA领域近期取得进展，仍存在两个关键问题：1）现有方法采用固定阈值进行伪标签生成，由于模型置信度在不同类别和域间存在差异，导致产生低质量伪标签；2）当前解决方案虽采用随机参数恢复来缓解灾难性遗忘，但其内在随机性削弱了关键信息的保存能力。为应对这些挑战，我们提出CTAOD方法，旨在提升检测模型在CTTA场景中的性能。借鉴先前CTTA工作的有效自适应思想，CTAOD基于均值教师框架构建，包含三个核心组件：首先，专为目标检测设计的对象级对比学习模块，利用教师模型的感兴趣区域特征提取对象级特征，并通过对比学习进行优化；其次，动态阈值策略根据预测置信度分数更新类别特定阈值，以提升伪标签质量；最后，我们设计数据驱动的随机恢复机制，通过将梯度作为随机掩码矩阵的权重来选择性重置非活跃参数，从而确保关键知识的保留。我们在四个目标检测CTTA任务上验证了方法的有效性，CTAOD在各项任务中均优于现有方法，特别是在Cityscapes到Cityscapes-C的CTTA任务中实现了3.0 mAP的性能提升。