# Evaluating the Progress of Deep Learning for Visual Relational Concepts

arXiv: https://arxiv.org/abs/2001.10857 · HTML: https://mlsanity.com/paper/2001.10857/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [A Review of Emerging Research Directions in Abstract Visual Reasoning](https://mlsanity.com/paper/2202.10284/) (2022)
- [What is the Visual Cognition Gap between Humans and Multimodal LLMs?](https://mlsanity.com/paper/2406.10424/) (2024)
- [Pix2Code: Learning to Compose Neural Visual Concepts as Programs](https://mlsanity.com/paper/2402.08280/) (2024)
- [Understanding the computational demands underlying visual reasoning](https://mlsanity.com/paper/2108.03603/) (2021)
- [GAMR: A Guided Attention Model for (visual) Reasoning](https://mlsanity.com/paper/2206.04928/) (2022)
- [Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems](https://mlsanity.com/paper/2508.12026/) (2025)
- [A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning](https://mlsanity.com/paper/2406.11061/) (2024)
- [Learning richness modulates equality reasoning in neural networks](https://mlsanity.com/paper/2503.09781/) (2025)
- [Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks](https://mlsanity.com/paper/2505.13391/) (2025)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) have become the state of the art method for image classification in the last ten years. Despite the fact that they achieve superhuman classification accuracy on many popular datasets, they often perform much worse on more abstract image classification tasks. We will show that these difficult tasks are linked to relational concepts from cognitive psychology and that despite progress over the last few years, such relational reasoning tasks still remain difficult for current neural network architectures. We will review deep learning research that is linked to relational concept learning, even if it was not originally presented from this angle. Reviewing the current literature, we will argue that some form of attention will be an important component of future systems to solve relational tasks. In addition, we will point out the shortcomings of currently used datasets, and we will recommend steps to make future datasets more relevant for testing systems on relational reasoning.