From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection

• URL: http://arxiv.org/abs/2606.23825v1
• Date: Mon, 22 Jun 2026 18:09:49 GMT
• Title: From Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection
• Abstract summary: Small object detection is bottlenecked by inherent feature scarcity of tiny targets.<n>We propose a paradigm textbfshift from spatial to spectral feature processing.<n>Our proposed textbfDERNet series outperforms YOLOv11 models under the same scale.
• Score: 5.01964718362187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. 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-play} modules -- Wavelet-Difference Gate (WDG), Log-Gabor Enhancer (LGE), and Frequency-Driven Head (FDHead) -- to systematically inject frequency-aware modulation into the backbone, neck, and head. This mechanism decouples feature modeling from resolution reduction, capturing discriminative high-frequency components to enable accurate localization with significantly reduced parameter redundancy; (3) Extensive validation on multi-domain benchmarks (VisDrone2019, UAVDT, TinyPerson, DOTAv1) demonstrating consistent gains. Notably, our proposed \textbf{DERNet} series outperforms YOLOv11 models under the same scale while requiring \textbf{only 1/6 of the parameters}, backed by rigorous spectral diagnostics and error decomposition analysis.

Related papers

• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification [6.738210148449625]
  Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples.<n>Existing metric learning-based methods typically rely solely on spatial domain features.<n>This paper proposes the Frequency-Enhanced Dual-Subspace Network (FEDSNet)
  arXiv  Detail & Related papers  (2026-04-16T12:54:29Z)
• UFO-DETR: Frequency-Guided End-to-End Detector for UAV Tiny Objects [7.1136620172045]
  Small target detection in UAV imagery faces significant challenges such as scale variations, dense distribution, and the dominance of small targets.<n>This paper proposes an end-to-end object detection framework, UFO-DETR, which integrates an LSKNet-based backbone network to optimize the receptive field and reduce the number of parameters.
  arXiv  Detail & Related papers  (2026-02-26T07:37:45Z)
• Noise-Robust Tiny Object Localization with Flows [63.60972031108944]
  We propose a noise-robust localization framework leveraging normalizing flows for flexible error modeling and uncertainty-guided optimization.<n>Our method captures complex, non-Gaussian prediction distributions through flow-based error modeling, enabling robust learning under noisy supervision.<n>An uncertainty-aware gradient modulation mechanism further suppresses learning from high-uncertainty, noise-prone samples, mitigating overfitting while stabilizing training.
  arXiv  Detail & Related papers  (2026-01-02T09:16:55Z)
• DFIR-DETR: Frequency Domain Enhancement and Dynamic Feature Aggregation for Cross-Scene Small Object Detection [16.16000521213211]
  Small object detection in UAV remote sensing images is difficult.<n>Current transformer-based detectors struggle with three critical issues.<n>We introduce DFIR-DETR to tackle these problems through dynamic feature aggregation combined with frequency-domain processing.
  arXiv  Detail & Related papers  (2025-12-08T01:25:10Z)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• High-Frequency Semantics and Geometric Priors for End-to-End Detection Transformers in Challenging UAV Imagery [6.902247657565531]
  We introduce HEDS-DETR, a holistically enhanced real-time Detection Transformer tailored for aerial scenes.<n>First, we propose a novel High-Frequency Enhanced Semantics Network (HFESNet) backbone, which yields highly discriminative features.<n>Second, our Efficient Small Object Pyramid (ESOP) counteracts information loss by efficiently fusing high-resolution features.<n>Third, we enhance decoder stability and localization precision with two synergistic components.
  arXiv  Detail & Related papers  (2025-07-01T14:56:56Z)
• DiffFormer: a Differential Spatial-Spectral Transformer for Hyperspectral Image Classification [3.271106943956333]
  Hyperspectral image classification (HSIC) has gained significant attention because of its potential in analyzing high-dimensional data with rich spectral and spatial information.<n>We propose the Differential Spatial-Spectral Transformer (DiffFormer) to address the inherent challenges of HSIC, such as spectral redundancy and spatial discontinuity.<n>Experiments on benchmark hyperspectral datasets demonstrate the superiority of DiffFormer in terms of classification accuracy, computational efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-12-23T07:21:41Z)
• UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation [17.289252835606533]
  Uncertainty-diffusion-model-based high-Frequency TransFormer network (UDHF2-Net) is the first to be proposed.<n> UDHF2-Net is a spatially-stationary-and-non-stationary high-frequency connection paradigm (SHCP)<n>Mask-and-geo-knowledge-based uncertainty diffusion module (MUDM) is a self-supervised learning strategy.<n>A frequency-wise semi-pseudo-Siamese UDHF2-Net is the first to be proposed to balance accuracy and complexity for change detection.
  arXiv  Detail & Related papers  (2024-06-23T15:03:35Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain.<n>Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage.<n>Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising [64.11157141177208]
  We propose a spectral enhanced rectangle Transformer to model the spatial and spectral correlation in hyperspectral images. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise.
  arXiv  Detail & Related papers  (2023-04-03T09:42:13Z)
• Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization [81.29406957201458]
  Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects. We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection. We propose to model the rotated objects as Gaussian distributions. We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
  arXiv  Detail & Related papers  (2022-09-22T07:50:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.