# Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification

arXiv: https://arxiv.org/abs/2106.05517 · HTML: https://mlsanity.com/paper/2106.05517/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 10 Jun 2021.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (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)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [Meta-Learning with Differentiable Convex Optimization](https://mlsanity.com/paper/1904.03758/) (2019)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Meta-learning with differentiable closed-form solvers](https://mlsanity.com/paper/1805.08136/) (2018)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning](https://mlsanity.com/paper/1805.10002/) (2018)
- [Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation](https://mlsanity.com/paper/2001.08735/) (2020)
- [CrossTransformers: spatially-aware few-shot transfer](https://mlsanity.com/paper/2007.11498/) (2020)
- [Negative Margin Matters: Understanding Margin in Few-shot Classification](https://mlsanity.com/paper/2003.12060/) (2020)
- [Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning](https://mlsanity.com/paper/1903.12290/) (2019)
- [Few-Shot Classification with Feature Map Reconstruction Networks](https://mlsanity.com/paper/2012.01506/) (2020)
- [Graph Prototypical Networks for Few-shot Learning on Attributed Networks](https://mlsanity.com/paper/2006.12739/) (2020)
- [Associative Alignment for Few-shot Image Classification](https://mlsanity.com/paper/1912.05094/) (2019)
- [Dense Classification and Implanting for Few-Shot Learning](https://mlsanity.com/paper/1903.05050/) (2019)
- [DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning](https://mlsanity.com/paper/2003.06777/) (2020)
- [Few-Shot Learning with Localization in Realistic Settings](https://mlsanity.com/paper/1904.08502/) (2019)
- [Asymmetric Distribution Measure for Few-shot Learning](https://mlsanity.com/paper/2002.00153/) (2020)
- [DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network](https://mlsanity.com/paper/2103.08160/) (2021)
- [Local Propagation for Few-Shot Learning](https://mlsanity.com/paper/2101.01480/) (2021)

## Built on by (most cited first, 18 of 18)

- [Disentangled Feature Representation for Few-shot Image Classification](https://mlsanity.com/paper/2109.12548/) (2021)
- [An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning](https://mlsanity.com/paper/2209.13777/) (2022)
- [SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning](https://mlsanity.com/paper/2303.09281/) (2023)
- [Clustered-patch Element Connection for Few-shot Learning](https://mlsanity.com/paper/2304.10093/) (2023)
- [MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks](https://mlsanity.com/paper/2305.10309/) (2023)
- [PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification](https://mlsanity.com/paper/2310.03517/) (2023)
- [Disentangled Representation Learning with Transmitted Information Bottleneck](https://mlsanity.com/paper/2311.01686/) (2023)
- [SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification](https://mlsanity.com/paper/2605.13672/) (2026)
- [Harnessing the Power of Local Representations for Few-Shot Classification](https://mlsanity.com/paper/2407.01967/) (2024)
- [Learning Primitive-aware Discriminative Representations for Few-shot Learning](https://mlsanity.com/paper/2208.09717/) (2022)
- [Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work](https://mlsanity.com/paper/2306.01929/) (2023)
- [Knowledge Transfer-Driven Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2306.10942/) (2023)
- [Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification](https://mlsanity.com/paper/2401.11724/) (2024)
- [Brain Inspired Adaptive Memory Dual-Net for Few-Shot Image Classification](https://mlsanity.com/paper/2503.07396/) (2025)
- [Hierarchical Mask-Enhanced Dual Reconstruction Network for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2506.20263/) (2025)
- [MetaLab: Few-Shot Game Changer for Image Recognition](https://mlsanity.com/paper/2507.22057/) (2025)
- [Color as the Impetus: Transforming Few-Shot Learner](https://mlsanity.com/paper/2507.22136/) (2025)
- [BATR-FST: Bi-Level Adaptive Token Refinement for Few-Shot Transformers](https://mlsanity.com/paper/2509.12768/) (2025)

## Abstract (arXiv)

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks not seen during training, given only a few examples. To handle the limited-data problem in few-shot regimes, recent methods tend to collectively use a set of local features to densely represent an image instead of using a mixed global feature. They generally explore a unidirectional query-to-support paradigm in FSL, e.g., find the nearest/optimal support feature for each query feature and aggregate these local matches for a joint classification. In this paper, we propose a new method Mutual Centralized Learning (MCL) to fully affiliate the two disjoint sets of dense features in a bidirectional paradigm. We associate each local feature with a particle that can bidirectionally random walk in a discrete feature space by the affiliations. To estimate the class probability, we propose the features' accessibility that measures the expected number of visits to the support features of that class in a Markov process. We relate our method to learning a centrality on an affiliation network and demonstrate its capability to be plugged in existing methods by highlighting centralized local features. Experiments show that our method achieves the state-of-the-art on both miniImageNet and tieredImageNet.