Evaluating the Progress of Deep Learning for Visual Relational Concepts
This is an incremental review that addresses the challenge of improving relational reasoning in AI systems for researchers and practitioners in computer vision.
The paper tackles the problem of deep learning's poor performance on abstract image classification tasks linked to relational concepts, showing that despite progress, these tasks remain difficult for current architectures and highlighting the need for attention mechanisms and better datasets.
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.