# Few-Shot Learning by Integrating Spatial and Frequency Representation

arXiv: https://arxiv.org/abs/2105.05348 · HTML: https://mlsanity.com/paper/2105.05348/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Low-Shot Learning from Imaginary Data](https://mlsanity.com/paper/1801.05401/) (2018)
- [Charting the Right Manifold: Manifold Mixup for Few-shot Learning](https://mlsanity.com/paper/1907.12087/) (2019)
- [Adversarial Feature Hallucination Networks for Few-Shot Learning](https://mlsanity.com/paper/2003.13193/) (2020)
- [DPGN: Distribution Propagation Graph Network for Few-shot Learning](https://mlsanity.com/paper/2003.14247/) (2020)
- [Leveraging the Feature Distribution in Transfer-based Few-Shot Learning](https://mlsanity.com/paper/2006.03806/) (2020)
- [Image Deformation Meta-Networks for One-Shot Learning](https://mlsanity.com/paper/1905.11641/) (2019)
- [Few-Shot Learning via Saliency-guided Hallucination of Samples](https://mlsanity.com/paper/1904.03472/) (2019)
- [Few-Shot Learning with Global Class Representations](https://mlsanity.com/paper/1908.05257/) (2019)
- [SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images](https://mlsanity.com/paper/2101.07422/) (2021)
- [Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks](https://mlsanity.com/paper/1906.11888/) (2019)
- [Compressing Convolutional Neural Networks](https://mlsanity.com/paper/1506.04449/) (2015)
- [Deep Feature Augmentation for Occluded Image Classification](https://mlsanity.com/paper/2011.00768/) (2020)
- [Band-limited Training and Inference for Convolutional Neural Networks](https://mlsanity.com/paper/1911.09287/) (2019)
- [Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection](https://mlsanity.com/paper/2007.06233/) (2020)
- [Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting](https://mlsanity.com/paper/2010.01664/) (2020)

## Built on by (most cited first, 11 of 11)

- [Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets](https://mlsanity.com/paper/2210.12333/) (2022)
- [Audio-Visual Transformer Based Crowd Counting](https://mlsanity.com/paper/2109.01926/) (2021)
- [Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2411.01432/) (2024)
- [The Self-Optimal-Transport Feature Transform](https://mlsanity.com/paper/2204.03065/) (2022)
- [Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation](https://mlsanity.com/paper/2207.08547/) (2022)
- [Multiple Classifiers Based Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2108.00610/) (2021)
- [The Balanced-Pairwise-Affinities Feature Transform](https://mlsanity.com/paper/2407.01467/) (2024)
- [Class-Specific Channel Attention for Few-Shot Learning](https://mlsanity.com/paper/2209.01332/) (2022)
- [Image edge enhancement for effective image classification](https://mlsanity.com/paper/2401.07028/) (2024)
- [FreqGRL: Suppressing Low-Frequency Bias and Mining High-Frequency Knowledge for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2511.06648/) (2025)
- [Spectral Analysis for Sparse Matrix Computation: Insights and Potential](https://mlsanity.com/paper/2608.29362/) (2026)

## Abstract (arXiv)

Human beings can recognize new objects with only a few labeled examples, however, few-shot learning remains a challenging problem for machine learning systems. Most previous algorithms in few-shot learning only utilize spatial information of the images. In this paper, we propose to integrate the frequency information into the learning model to boost the discrimination ability of the system. We employ Discrete Cosine Transformation (DCT) to generate the frequency representation, then, integrate the features from both the spatial domain and frequency domain for classification. The proposed strategy and its effectiveness are validated with different backbones, datasets, and algorithms. Extensive experiments demonstrate that the frequency information is complementary to the spatial representations in few-shot classification. The classification accuracy is boosted significantly by integrating features from both the spatial and frequency domains in different few-shot learning tasks.