Multi-Label Contrastive Learning for Abstract Visual Reasoning

Understand

For a long time the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence.

• Recent advances in application of deep learning (DL) methods led, as in many other domains, to surpassing human abstract reasoning performance, specifically in the most popular type of such problems - the Raven's Progressive Matrices (RPMs).
• While the efficacy of DL systems is indeed impressive, the way they approach the RPMs is very different from that of humans.
• State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on identification of the rules / concepts underlying the RPM (or generally a visual reasoning task) to be solved.