Quantifying Context Bias in Domain Adaptation for Object Detection

Abstract

Domain adaptation for object detection (DAOD) has become essential to counter performance degradation caused by distribution shifts between training and deployment domains. However, a critical factor influencing DAOD—context bias resulting from learned foreground-background (FG–BG) association—remains underexplored. In this work, we present the first comprehensive empirical and causal analysis specifically targeting context bias in DAOD. We address three key questions regarding FG–BG association in object detection: (a) whether FG–BG association is encoded during training, (b) whether there is a causal relationship between FG–BG association and detection performance, and (c) whether FG–BG association affects DAOD. To examine how models capture FG–BG association, we analyze class-wise and feature-wise performance degradation using background masking and feature perturbation, measured via change in accuracy (defined as drop rate). To explore the causal role of FG–BG association, we apply do-calculus to FG–BG pairs guided by class activation mapping (CAM). To quantify the causal influence of FG–BG association across domains, we propose a novel metric—Domain Association Gradient—defined as the ratio of drop rate to maximum mean discrepancy (MMD). Through systematic experiments involving background masking, feature-level perturbations, and CAM, we reveal that convolution-based object detection models encode FG–BG association. The association substantially impacts detection performance, particularly under domain shifts where background information significantly diverges. Our results demonstrate that context bias not only exists but also causally undermines the generalization capabilities of object detection models across domains. Furthermore, we validate these findings across multiple models and datasets, including state-of-the-art architectures such as ALDI++. This study highlights the necessity of addressing context bias explicitly in DAOD frameworks, providing insights that pave the way for developing more robust and generalizable object detection systems.