# Few-shot Visual Reasoning with Meta-analogical Contrastive Learning

arXiv: https://arxiv.org/abs/2007.12020 · HTML: https://mlsanity.com/paper/2007.12020/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 19 of 19)

- [A Broad Study on the Transferability of Visual Representations with Contrastive Learning](https://mlsanity.com/paper/2103.13517/) (2021)
- [Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices](https://mlsanity.com/paper/2201.12382/) (2022)
- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [Transductive Few-Shot Classification on the Oblique Manifold](https://mlsanity.com/paper/2108.04009/) (2021)
- [Effective Abstract Reasoning with Dual-Contrast Network](https://mlsanity.com/paper/2205.13720/) (2022)
- [In-Context Analogical Reasoning with Pre-Trained Language Models](https://mlsanity.com/paper/2305.17626/) (2023)
- [Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices](https://mlsanity.com/paper/2303.11730/) (2023)
- [Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction](https://mlsanity.com/paper/2111.12301/) (2021)
- [ViTCN: Vision Transformer Contrastive Network For Reasoning](https://mlsanity.com/paper/2403.09962/) (2024)
- [Data augmentation by morphological mixup for solving Raven's Progressive Matrices](https://mlsanity.com/paper/2103.05222/) (2021)
- [Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction](https://mlsanity.com/paper/2302.04238/) (2023)
- [Self-supervised Spatial Reasoning on Multi-View Line Drawings](https://mlsanity.com/paper/2104.13433/) (2021)
- [Naive Few-Shot Learning: Uncovering the fluid intelligence of machines](https://mlsanity.com/paper/2205.12013/) (2022)
- [Learning Abstract Visual Reasoning via Task Decomposition: A Case Study in Raven Progressive Matrices](https://mlsanity.com/paper/2308.06528/) (2023)
- [Neural Force Field: Few-shot Learning of Generalized Physical Reasoning](https://mlsanity.com/paper/2502.08987/) (2025)
- [Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training](https://mlsanity.com/paper/2205.01924/) (2022)
- [Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis](https://mlsanity.com/paper/2604.05649/) (2026)
- [Transformer See, Transformer Do: Copying as an Intermediate Step in Learning Analogical Reasoning](https://mlsanity.com/paper/2604.06501/) (2026)
- [DIRCR: Dual-Inference Rule-Contrastive Reasoning for Solving RAVENs](https://mlsanity.com/paper/2604.17584/) (2026)

## Abstract (arXiv)

While humans can solve a visual puzzle that requires logical reasoning by observing only few samples, it would require training over large amount of data for state-of-the-art deep reasoning models to obtain similar performance on the same task. In this work, we propose to solve such a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning, which is a unique human ability to identify structural or relational similarity between two sets. Specifically, given training and test sets that contain the same type of visual reasoning problems, we extract the structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning. We repeatedly apply this process with slightly modified queries of the same problem under the assumption that it does not affect the relationship between a training and a test sample. This allows to learn the relational similarity between the two samples in an effective manner even with a single pair of samples. We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce. We further meta-learn our analogical contrastive learning model over the same tasks with diverse attributes, and show that it generalizes to the same visual reasoning problem with unseen attributes.