# Evaluating the Progress of Deep Learning for Visual Relational Concepts

Source: [Evaluating the Progress of Deep Learning for Visual Relational Concepts](<https://www.sota2.com/research/paper/68fa6acf1e209131cea2a73e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:33:16.767Z

Authors: Sebastian Stabinger, Peer David, Justus Piater, Antonio Rodr\\'iguez-S\\'anchez

Published: 2020-01-29T14:21:34.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.10857>)

[PDF](<https://arxiv.org/pdf/2001.10857.pdf>)