FOUND: Fourier-based von Mises Distribution for Robust Single Domain Generalization in Object Detection
This work addresses robustness in object detection for unseen domains, which is an incremental improvement over prior methods.
The paper tackles the problem of single domain generalization for object detection by proposing a framework that integrates von Mises-Fisher distribution and Fourier transformation into a CLIP-guided pipeline, resulting in outperforming the existing state-of-the-art method on the weather-driving benchmark.
Single Domain Generalization (SDG) for object detection aims to train a model on a single source domain that can generalize effectively to unseen target domains. While recent methods like CLIP-based semantic augmentation have shown promise, they often overlook the underlying structure of feature distributions and frequency-domain characteristics that are critical for robustness. In this paper, we propose a novel framework that enhances SDG object detection by integrating the von Mises-Fisher (vMF) distribution and Fourier transformation into a CLIP-guided pipeline. Specifically, we model the directional features of object representations using vMF to better capture domain-invariant semantic structures in the embedding space. Additionally, we introduce a Fourier-based augmentation strategy that perturbs amplitude and phase components to simulate domain shifts in the frequency domain, further improving feature robustness. Our method not only preserves the semantic alignment benefits of CLIP but also enriches feature diversity and structural consistency across domains. Extensive experiments on the diverse weather-driving benchmark demonstrate that our approach outperforms the existing state-of-the-art method.