Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection

Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, Jiandong Tian

Abstract: 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 per-formance significantly. However, existing methods attempt to extract domain-invariant features, neglecting that the bi-ased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end, we pro-pose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically, we formulate SDG in object detection from a causal perspective and construct a Struc-tural Causal Model (SCM) to analyze the data bias andfeature bias in the task, which are caused by scene confounders and object attribute confounders. Based on the SCM, we de-sign a Global-Local Transformation module for data aug-mentation, which effectively simulates domain diversity and mitigates the data bias. Additionally, we introduce a Causal Attention Learning module that incorporates a designed at-tention 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 fur-ther alleviates the negative impact of object attribute con-founders. 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.