# Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

arXiv: https://arxiv.org/abs/2001.03919 · HTML: https://mlsanity.com/paper/2001.03919/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2020.

## Built on by (most cited first, 30 of 31)

- [Contrastive Laplacian Eigenmaps](https://mlsanity.com/paper/2201.05493/) (2022)
- [Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments](https://mlsanity.com/paper/2105.08149/) (2021)
- [Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition](https://mlsanity.com/paper/2210.16820/) (2022)
- [Meta-Learning for Multi-Label Few-Shot Classification](https://mlsanity.com/paper/2110.13494/) (2021)
- [Task Discrepancy Maximization for Fine-grained Few-Shot Classification](https://mlsanity.com/paper/2207.01376/) (2022)
- [Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid](https://mlsanity.com/paper/2210.16834/) (2022)
- [Attribute Prototype Network for Any-Shot Learning](https://mlsanity.com/paper/2204.01208/) (2022)
- [Hybrid Graph Neural Networks for Few-Shot Learning](https://mlsanity.com/paper/2112.06538/) (2021)
- [Sparse Spatial Transformers for Few-Shot Learning](https://mlsanity.com/paper/2109.12932/) (2021)
- [Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring](https://mlsanity.com/paper/2302.07689/) (2023)
- [Multi-level Second-order Few-shot Learning](https://mlsanity.com/paper/2201.05916/) (2022)
- [Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning](https://mlsanity.com/paper/2205.09995/) (2022)
- [Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection](https://mlsanity.com/paper/2210.16897/) (2022)
- [Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment](https://mlsanity.com/paper/2402.04599/) (2024)
- [Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning](https://mlsanity.com/paper/2109.01295/) (2021)
- [tSF: Transformer-based Semantic Filter for Few-Shot Learning](https://mlsanity.com/paper/2211.00868/) (2022)
- [Progressive Co-Attention Network for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.08527/) (2021)
- [Exploring Category-correlated Feature for Few-shot Image Classification](https://mlsanity.com/paper/2112.07224/) (2021)
- [Self-Attention Message Passing for Contrastive Few-Shot Learning](https://mlsanity.com/paper/2210.06339/) (2022)
- [SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning](https://mlsanity.com/paper/2303.09281/) (2023)
- [Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification](https://mlsanity.com/paper/2308.00093/) (2023)
- [Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels](https://mlsanity.com/paper/2401.15500/) (2024)
- [Simple Dialogue System with AUDITED](https://mlsanity.com/paper/2110.11881/) (2021)
- [Towards a Robust Differentiable Architecture Search under Label Noise](https://mlsanity.com/paper/2110.12197/) (2021)
- [Self-Supervised Class-Cognizant Few-Shot Classification](https://mlsanity.com/paper/2202.08149/) (2022)
- [Semantic Novelty Detection via Relational Reasoning](https://mlsanity.com/paper/2207.08699/) (2022)
- [BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis](https://mlsanity.com/paper/2606.29200/) (2026)
- [TLRM: Task-level Relation Module for GNN-based Few-Shot Learning](https://mlsanity.com/paper/2101.09840/) (2021)
- [Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure](https://mlsanity.com/paper/2405.00631/) (2024)
- [Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2407.02123/) (2024)

## Abstract (arXiv)

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. To paraphrase, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that in fact both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image representations and correct the relation understanding in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several the state-of-the-art models on publicly available datasets.