Fourier-KAN: Feature Distribution Decomposition and Recombination for Unknown-Domain Object Detection

Single-domain Generalized Object Detection (Single-DGOD) is recently proposed, aiming to transfer a detector to multiple unknown domains never seen during training. For this task, the challenge mainly lies in how to utilize the single data distribution from the source domain to generalize across multiple unknown domains with diverse data distributions. Accordingly, the challenge could be addressed by expanding the data distribution of the source domain. In this paper, we propose feature recombination from a frequency perspective to generate a series of recombined features that exhibit diversity in style and rich variation in content features. Specifically, we propose a new method, Fourier-KAN Feature Recombination, which utilizes the Fast Fourier Transform (FFT) to decompose features into amplitude and phase components. Then we apply the Kolmogorov-Arnold theorem to further decompose these components into linear combinations of multiple base distributions. Finally, through multi-level recombination, we generate a series of recombined features with diverse distributions, effectively emulating deep cross-domain variations in feature levels and strengthening the model's generalization ability to unknown domains. Our method demonstrates strong adaptability to both two-stage and single-stage detection frameworks. Experimental results show that on the Diverse Weather and Real-to-Art benchmarks, our approach not only achieves outstanding detection accuracy but also significantly enhances the model's generalization ability, all while maintaining excellent real-time performance. Our code is available at https://github.com/2490o/Fourier-KAN.