  Raven's Progressive Matrices (RPMs) are frequently used in evaluating human's
visual reasoning ability. Researchers have made considerable efforts in
developing systems to automatically solve the RPM problem, often through a
black-box end-to-end convolutional neural network for both visual recognition
and logical reasoning tasks. Based on the two intrinsic natures of RPM problem,
visual recognition and logical reasoning, we propose a Two-stage Rule-Induction
Visual Reasoner (TRIVR), which consists of a perception module and a reasoning
module, to tackle the challenges of real-world visual recognition and
subsequent logical reasoning tasks, respectively. For the reasoning module, we
further propose a "2+1" formulation that models human's thinking in solving
RPMs and significantly reduces the model complexity. It derives a reasoning
rule from each RPM sample, which is not feasible for existing methods. As a
result, the proposed reasoning module is capable of yielding a set of reasoning
rules modeling human in solving the RPM problems. To validate the proposed
method on real-world applications, an RPM-like Video Prediction (RVP) dataset
is constructed, where visual reasoning is conducted on RPMs constructed using
real-world video frames. Experimental results on various RPM-like datasets
demonstrate that the proposed TRIVR achieves a significant and consistent
performance gain compared with the state-of-the-art models.
