强增强会模糊类别信息，WSCo通过弱增强图像补救损失的语义。
Source-Free Domain Adaptive Object Detection with Semantics Compensation
- 用弱增强图像作为锚点，补偿强增强中丢失的类别语义。
- 在COCO-to-Pascal、Cityscapes等基准上提升检测精度。
- 可插件式集成，适用于各类源域自适应检测模型。
基于均值教师的源域自适应目标检测（SFOD）方法依赖强数据增强，实现基于一致性的自监督优化。然而，理论分析与实证观察表明，强增强会无意中抹除与类别相关的关键特征，导致类别间混淆。为此，本文提出弱到强语义补偿（WSCo），利用保留完整语义的弱增强图像作为锚点，丰富其对应强增强样本的特征空间，从而在运行时补偿因强增强丢失的类别相关语义。WSCo可作为通用插件模块，轻松集成至现有SFOD流程。大量实验验证了强增强对检测性能的负面影响，以及WSCo在标准基准（如COCO-to-Pascal、Cityscapes）上提升已有检测模型性能的有效性。
原文摘要 · Abstract (English)
Strong data augmentation is a fundamental component of state-of-the-art mean teacher-based Source-Free domain adaptive Object Detection (SFOD) methods, enabling consistency-based self-supervised optimization along weak augmentation. However, our theoretical analysis and empirical observations reveal a critical limitation: strong augmentation can inadvertently erase class-relevant components, leading to artificial inter-category confusion. To address this issue, we introduce Weak-to-strong Semantics Compensation (WSCo), a novel remedy that leverages weakly augmented images, which preserve full semantics, as anchors to enrich the feature space of their strongly augmented counterparts. Essentially, this compensates for the class-relevant semantics that may be lost during strong augmentation on the fly. Notably, WSCo can be implemented as a generic plug-in, easily integrable with any existing SFOD pipelines. Extensive experiments validate the negative impact of strong augmentation on detection performance, and the effectiveness of WSCo in enhancing the performance of previous detection models on standard benchmarks.
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