# Spatial frequency information fusion network for few-shot learning

arXiv: https://arxiv.org/abs/2506.18364 · HTML: https://mlsanity.com/paper/2506.18364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Few-Shot Classification with Feature Map Reconstruction Networks](https://mlsanity.com/paper/2012.01506/) (2020)
- [Regularization for Deep Learning: A Taxonomy](https://mlsanity.com/paper/1710.10686/) (2017)
- [Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification](https://mlsanity.com/paper/2204.04567/) (2022)
- [BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification](https://mlsanity.com/paper/2011.14311/) (2020)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification](https://mlsanity.com/paper/2106.06988/) (2021)
- [Dynamic Accumulated Attention Map for Interpreting Evolution of Decision-Making in Vision Transformer](https://mlsanity.com/paper/2503.14640/) (2025)
- [Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection](https://mlsanity.com/paper/2309.13922/) (2023)

## Abstract (arXiv)

The objective of Few-shot learning is to fully leverage the limited data resources for exploring the latent correlations within the data by applying algorithms and training a model with outstanding performance that can adequately meet the demands of practical applications. In practical applications, the number of images in each category is usually less than that in traditional deep learning, which can lead to over-fitting and poor generalization performance. Currently, many Few-shot classification models pay more attention to spatial domain information while neglecting frequency domain information, which contains more feature information. Ignoring frequency domain information will prevent the model from fully exploiting feature information, which would effect the classification performance. Based on conventional data augmentation, this paper proposes an SFIFNet with innovative data preprocessing. The key of this method is enhancing the accuracy of image feature representation by integrating frequency domain information with spatial domain information. The experimental results demonstrate the effectiveness of this method in enhancing classification performance.