As object detection tasks progress rapidly, fine-grained detection flourishes as a promising extension. Fine-grained recognition naturally demands high-quality detail signals; however, existing fine-grained detectors, built upon the mainstream detection paradigm, struggle to simultaneously address the challenges of insufficient original signals and the loss of critical signals, resulting in inferior performance. We argue that language signals with advanced semantic knowledge can provide valuable information for fine-grained objects, as well as the frequency domain exhibits greater flexibility in suppressing and enhancing signals; then, we propose a fine-grained aircraft detector by integrating language knowledge and frequency representations into the one-stage detection paradigm. Concretely, by considering both original signals and deep feature signals, we develop three components, including an adaptive frequency augmentation branch (AFAB), a content-aware global features intensifier (CGFI), and a fine-grained text-image interactive feeder (FTIF), to facilitate perceiving and retaining critical signals throughout pivotal detection stages. The AFAB adaptively processes image patches according to their frequency characteristics in the Fourier domain, thus thoroughly mining critical visual content in the data space; the CGFI employs content-aware frequency filtering to enhance global features, allowing for generating an information-rich feature space; the FTIF introduces text knowledge to describe visual differences among fine-grained categories, conveying robust semantic priors from language signals to visual spaces via multimodal interaction for information supplement. Extensive experiments conducted on optical and SAR images demonstrate the superior performance of the proposed fine-grained detector, especially the FTIF, which can be plugged into most existing one-stage detectors to boost their fine-grained recognition performance significantly.
