# Data augmentation by morphological mixup for solving Raven's Progressive Matrices

arXiv: https://arxiv.org/abs/2103.05222 · HTML: https://mlsanity.com/paper/2103.05222/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [RAVEN: A Dataset for Relational and Analogical Visual rEasoNing](https://mlsanity.com/paper/1903.02741/) (2019)
- [Measuring abstract reasoning in neural networks](https://mlsanity.com/paper/1807.04225/) (2018)
- [MixUp as Locally Linear Out-Of-Manifold Regularization](https://mlsanity.com/paper/1809.02499/) (2018)
- [Data Augmentation by Pairing Samples for Images Classification](https://mlsanity.com/paper/1801.02929/) (2018)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Regularizing Deep Networks with Semantic Data Augmentation](https://mlsanity.com/paper/2007.10538/) (2020)
- [Stratified Rule-Aware Network for Abstract Visual Reasoning](https://mlsanity.com/paper/2002.06838/) (2020)
- [Learning Perceptual Inference by Contrasting](https://mlsanity.com/paper/1912.00086/) (2019)
- [Improved Mixed-Example Data Augmentation](https://mlsanity.com/paper/1805.11272/) (2018)
- [Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"](https://mlsanity.com/paper/2006.11524/) (2020)
- [Abstract Reasoning with Distracting Features](https://mlsanity.com/paper/1912.00569/) (2019)
- [Feature Space Transfer for Data Augmentation](https://mlsanity.com/paper/1801.04356/) (2018)
- [MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning](https://mlsanity.com/paper/1908.10059/) (2019)
- [On the Importance of Visual Context for Data Augmentation in Scene Understanding](https://mlsanity.com/paper/1809.02492/) (2018)
- [Few-shot Visual Reasoning with Meta-analogical Contrastive Learning](https://mlsanity.com/paper/2007.12020/) (2020)
- [The Forgettable-Watcher Model for Video Question Answering](https://mlsanity.com/paper/1705.01253/) (2017)
- [Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation](https://mlsanity.com/paper/2002.02598/) (2020)

## Built on by (most cited first, 2 of 2)

- [Predictive Reasoning with Augmented Anomaly Contrastive Learning for Compositional Visual Relations](https://mlsanity.com/paper/2603.01125/) (2026)
- [DIRCR: Dual-Inference Rule-Contrastive Reasoning for Solving RAVENs](https://mlsanity.com/paper/2604.17584/) (2026)

## Abstract (arXiv)

Raven's Progressive Matrices (RPMs) are frequently used in testing human's visual reasoning ability. Recent advances of RPM-like datasets and solution models partially address the challenges of visually understanding the RPM questions and logically reasoning the missing answers. In view of the poor generalization performance due to insufficient samples in RPM datasets, we propose an effective scheme, namely Candidate Answer Morphological Mixup (CAM-Mix). CAM-Mix serves as a data augmentation strategy by gray-scale image morphological mixup, which regularizes various solution methods and overcomes the model overfitting problem. By creating new negative candidate answers semantically similar to the correct answers, a more accurate decision boundary could be defined. By applying the proposed data augmentation method, a significant and consistent performance improvement is achieved on various RPM-like datasets compared with the state-of-the-art models.