Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization

Problem
This paper addresses the challenge of domain generalization in object detection, particularly the performance degradation caused by distributional gaps between source and target domains. The authors highlight that existing models often rely on confounders such as illumination and style, leading to spurious correlations that impair generalization. This work is presented as a preprint, indicating it has not yet undergone peer review.

Method
The authors introduce a novel framework called Bridge, which integrates causal inference into the domain generalization process. The core technical contribution involves learning low-rank bases for front-door adjustment, which effectively blocks the influence of confounders and mitigates spurious correlations. The architecture is designed to refine representations by filtering out redundant and task-irrelevant components. Bridge can be integrated with various Vision Foundation Models (VFMs), including discriminative models like DINOv2/3 and SAM, as well as generative models such as Stable Diffusion. The training process and specific loss functions are not detailed in the abstract, but the framework’s adaptability to different model types suggests a flexible training regime.

Results
The authors conducted extensive experiments across multiple domain generalization benchmarks, including Cross-Camera, Adverse Weather, Real-to-Artistic, and Diverse Weather Datasets, as well as a newly introduced benchmark, Diverse Weather DroneVehicle. Bridge outperformed previous state-of-the-art methods, achieving significant improvements in detection accuracy. While specific numerical results are not provided in the abstract, the emphasis on superiority suggests effect sizes that are substantial enough to warrant attention from the research community.

Limitations
The authors acknowledge that while Bridge effectively addresses confounder effects, the reliance on low-rank bases may introduce limitations in scenarios with highly complex confounder structures. Additionally, the integration with VFMs may require careful tuning to optimize performance across different architectures. The paper does not discuss the computational overhead introduced by the causal inference component, which could impact scalability in real-world applications.

Why it matters
This work has significant implications for advancing domain generalization techniques in object detection, particularly in scenarios with limited data. By incorporating causal inference, Bridge provides a robust framework for mitigating spurious correlations, which is critical for deploying models in diverse and unpredictable environments. The ability to integrate with existing VFMs also suggests a pathway for enhancing the generalization capabilities of current state-of-the-art models, potentially influencing future research directions in both causal inference and domain adaptation.

Authors: Mingbo Hong, Feng Liu, Caroline Gevaert, George Vosselman, Hao Cheng
Source: arXiv:2604.26820
URL: https://arxiv.org/abs/2604.26820v1