HGM: A General High-Order Spatial and Spectral Global Feature Fusion Module for Visual Multitasking

Abstract

Recent computer vision research has mainly focused on designing efficient network architectures, with limited exploration of high- and low-frequency information in the high-order frequency domain. This study introduces a novel approach utilizing spatial and frequency domain information to design a high-order global feature fusion module (HGM) and develop a specialized remote sensing detection network, HGNet. HGM leverages cyclic convolution to achieve arbitrary high-order features, overcoming the second-order limitation of transformers. Furthermore, HGM integrates cyclic convolution and fast Fourier transform, utilizing the former to capture interaction information between high-order spatial and channel domains and the latter to transform high-order features from spatial to frequency domain for global information extraction. This combination fundamentally addresses the issue of long-distance dependency in convolutions and avoids quadratic growth in computational complexity. Moreover, we have constructed an information truncation gate to minimize high-order redundant features, achieving a “win–win” scenario for network accuracy and parameter efficiency. In addition, HGM acts as a plug-and-play module, boosting performance when integrated into various networks. Experimental findings reveal that HGNet achieves a 93.0% $\text{mAP}_{\text{0.5}}$ with just 12.1M parameters on the HRSID remote sensing ship detection dataset. In addition, applying HGM enhances a performance in CIFAR100 classification and WHDLD remote sensing segmentation tasks.

Keywords

• Convolutional neural networks (CNNs)
• feature aggregation
• global frequency domain features
• high-order feature interaction
• remote sensing ship detection
• transformer architecture