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Recovering these fragile cues within the spatial domain is notoriously difficult, as it often requires computationally expensive architectural upscaling that inadvertently amplifies background noise. To bridge this gap, we propose a paradigm \\textbf{shift from spatial to spectral} feature processing, introducing a holistic solution with the following novelty: (1) A versatile \\textbf{Frequency-Guided Feature Representation framework} that generalizes across diverse detector architectures (both CNN and Transformer-based), offering a robust alternative to spatial-only feature extraction; (2) The unified \\textbf{Decompose--Enhance--Reconstruct (DER)} operator, instantiated via three \\textbf{lightweight, plug-and","title":"From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection","url":"https://arxiv.org/abs/2606.23825","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.23825v1 Announce Type: cross \nAbstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details. Recovering these fragile cues within the spatial domain is notoriously difficult, as it often requires computationally expensive architectural upscaling that inadvertently amplifies background noise. 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