Convolutional neural networks have become the state-of-the-art method for image classification in the last 10 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.

中文翻译：

---

评估视觉关系概念深度学习的进展。


过去 10 年，卷积神经网络已成为最先进的图像分类方法。尽管它们在许多流行的数据集上实现了超人的分类精度，但它们在更抽象的图像分类任务上的表现通常要差得多。我们将证明这些困难的任务与认知心理学中的关系概念相关，尽管过去几年取得了进展，但此类关系推理任务对于当前的神经网络架构来说仍然很困难。我们将回顾与关系概念学习相关的深度学习研究，即使它最初不是从这个角度提出的。回顾当前的文献，我们认为某种形式的注意力将成为未来系统解决关系任务的重要组成部分。此外，我们将指出当前使用的数据集的缺点，并且我们将建议采取哪些步骤，使未来的数据集与关系推理测试系统更加相关。