Single-source domain generalization (SDG) for object detection is a
challenging yet essential task as the distribution bias of the unseen domain
degrades the algorithm performance significantly. However, existing methods
attempt to extract domain-invariant features, neglecting that the biased data
leads the network to learn biased features that are non-causal and poorly
generalizable. To this end, we propose an Unbiased Faster R-CNN (UFR) for
generalizable feature learning. Specifically, we formulate SDG in object
detection from a causal perspective and construct a Structural Causal Model
(SCM) to analyze the data bias and feature bias in the task, which are caused
by scene confounders and object attribute confounders. Based on the SCM, we
design a Global-Local Transformation module for data augmentation, which
effectively simulates domain diversity and mitigates the data bias.
Additionally, we introduce a Causal Attention Learning module that incorporates
a designed attention invariance loss to learn image-level features that are
robust to scene confounders. Moreover, we develop a Causal Prototype Learning
module with an explicit instance constraint and an implicit prototype
constraint, which further alleviates the negative impact of object attribute
confounders. Experimental results on five scenes demonstrate the prominent
generalization ability of our method, with an improvement of 3.9% mAP on the
Night-Clear scene.

提出了一种单源域泛化的方法，通过构建结构因果模型来分析任务中的数据偏差和特征偏差，并设计了全局 - 本地转换模块和因果关注学习模块来增强算法的泛化能力。在五个场景的实验中，该方法取得了明显的改进，夜晚晴朗场景的 mAP 提高了 3.9%。