# Spatial Contrastive Learning for Few-Shot Classification

arXiv: https://arxiv.org/abs/2012.13831 · HTML: https://mlsanity.com/paper/2012.13831/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2020.

## Built on by (most cited first, 17 of 17)

- [Rectifying the Shortcut Learning of Background for Few-Shot Learning](https://mlsanity.com/paper/2107.07746/) (2021)
- [Few-Shot Classification with Contrastive Learning](https://mlsanity.com/paper/2209.08224/) (2022)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [On the Importance of Distractors for Few-Shot Classification](https://mlsanity.com/paper/2109.09883/) (2021)
- [Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images](https://mlsanity.com/paper/2105.11874/) (2021)
- [Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning](https://mlsanity.com/paper/2112.10982/) (2021)
- [ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning](https://mlsanity.com/paper/2304.13287/) (2023)
- [Improved Region Proposal Network for Enhanced Few-Shot Object Detection](https://mlsanity.com/paper/2308.07535/) (2023)
- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [WLD-Reg: A Data-dependent Within-layer Diversity Regularizer](https://mlsanity.com/paper/2301.01352/) (2023)
- [ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection](https://mlsanity.com/paper/2105.03037/) (2021)
- [Prototypical Contrastive Learning For Improved Few-Shot Audio Classification](https://mlsanity.com/paper/2509.10074/) (2025)
- [Multi-Level Contrastive Learning for Few-Shot Problems](https://mlsanity.com/paper/2107.07608/) (2021)
- [Disentangled Generation with Information Bottleneck for Few-Shot Learning](https://mlsanity.com/paper/2211.16185/) (2022)
- [CP-VoteNet: Contrastive Prototypical VoteNet for Few-Shot Point Cloud Object Detection](https://mlsanity.com/paper/2408.17036/) (2024)
- [Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures](https://mlsanity.com/paper/2404.06080/) (2024)
- [Rethinking the Sample Relations for Few-Shot Classification](https://mlsanity.com/paper/2501.13418/) (2025)

## Abstract (arXiv)

In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent regularizer to promote more general and transferable features. In particular, we present a novel attention-based spatial contrastive objective to learn locally discriminative and class-agnostic features. As a result, our approach overcomes some of the limitations of the cross-entropy loss, such as its excessive discrimination towards seen classes, which reduces the transferability of features to unseen classes. With extensive experiments, we show that the proposed method outperforms state-of-the-art approaches, confirming the importance of learning good and transferable embeddings for few-shot learning.