Quantifying Context Bias in Domain Adaptation for Object Detection

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) associations - has remained underexplored. We address three key questions regarding FG BG associations in object detection: are FG-BG associations encoded during the training, is there a causal relationship between FG-BG associations and detection performance, and is there an effect of FG-BG association on DAOD. To examine how models capture FG BG associations, we analyze class-wise and feature-wise performance degradation using background masking and feature perturbation, measured via change in accuracies (defined as drop rate). To explore the causal role of FG-BG associations, we apply do-calculus on FG-BG pairs guided by class activation mapping (CAM). To quantify the causal influence of FG-BG associations 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 associations. Our results demonstrate that context bias not only exists but 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.