mlsanityData to 7 Oct 2026

Adaptive receptive field-based spatial-frequency feature reconstruction network for fine-grained few-shot image classification

arXiv 2604.16936 ↗First posted 18 Apr 2026cs.CV cs.AIcited by 2

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Abstract (arXiv)

Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field-based spatial-frequency feature reconstruction network (ARF-SFR-Net) is proposed. The designed ARF-SFR-Net has the capability to adaptively determine receptive field sizes for obtaining spatial and frequency features, and effectively fuse them for reconstruction and FSFGIC tasks. The designed ARF-SFR-Net can be easily embedded into a given episodic training mechanism for end-to-end training from scratch. Extensive experiments on multiple FSFGIC benchmarks demonstrate the effectiveness and superiority of the proposed ARF-SFR-Net over state-of-the-art approaches. The code is available at: https://github.com/ICL-SUST/ARF-SFR-Net.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 84 references that are in the map. First posted on arXiv 18 Apr 2026.

Builds on

21 of 84 references.

References · 21

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  2. 2017Prototypical Networks for Few-shot Learningcited by 3,001
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9 more
  1. 2024Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learningcited by 11
  2. 2022Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classificationcited by 9
  3. 2023Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detectioncited by 6
  4. 2022Few-Shot Learning of Compact Models via Task-Specific Meta Distillationcited by 5
  5. 2023Task-Oriented Channel Attention for Fine-Grained Few-Shot Classificationcited by 4
  6. 2026Meningioma Analysis and Diagnosis using Limited Labeled Samplescited by 4
  7. 2024Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Modelscited by 3
  8. 2024Harnessing the Power of Local Representations for Few-Shot Classificationcited by 1
  9. 2025Visual Explanation via Similar Feature Activation for Metric Learningcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury predictioncited by 1
  2. 2026Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classificationcited by 0