A dual-stream network combining CNN and vision transformer features, followed by a rule extractor, achieves state-of-the-art accuracy on multiple Raven's Progressive Matrices benchmarks, including large gains on out-of-distribution PGM subsets.
IQ of Neural Networks
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abstract
IQ tests are an accepted method for assessing human intelligence. The tests consist of several parts that must be solved under a time constraint. Of all the tested abilities, pattern recognition has been found to have the highest correlation with general intelligence. This is primarily because pattern recognition is the ability to find order in a noisy environment, a necessary skill for intelligent agents. In this paper, we propose a convolutional neural network (CNN) model for solving geometric pattern recognition problems. The CNN receives as input multiple ordered input images and outputs the next image according to the pattern. Our CNN is able to solve problems involving rotation, reflection, color, size and shape patterns and score within the top 5% of human performance.
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Learning Visual Abstract Reasoning through Dual-Stream Networks
A dual-stream network combining CNN and vision transformer features, followed by a rule extractor, achieves state-of-the-art accuracy on multiple Raven's Progressive Matrices benchmarks, including large gains on out-of-distribution PGM subsets.