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Raven's Progressive Matrices (RPMs) are frequently used in evaluating human'svisual reasoning ability. Researchers have made considerable efforts indeveloping systems to automatically solve the RPM problem, often through ablack-box end-to-end convolutional neural network for both visual recognitionand logical reasoning tasks. Based on the two intrinsic natures of RPM problem,visual recognition and logical reasoning, we propose a Two-stage Rule-InductionVisual Reasoner (TRIVR), which consists of a perception module and a reasoningmodule, to tackle the challenges of real-world visual recognition andsubsequent logical reasoning tasks, respectively. For the reasoning module, wefurther propose a "2+1" formulation that models human's thinking in solvingRPMs and significantly reduces the model complexity. It derives a reasoningrule from each RPM sample, which is not feasible for existing methods. As aresult, the proposed reasoning module is capable of yielding a set of reasoningrules modeling human in solving the RPM problems. To validate the proposedmethod on real-world applications, an RPM-like Video Prediction (RVP) datasetis constructed, where visual reasoning is conducted on RPMs constructed usingreal-world video frames. Experimental results on various RPM-like datasetsdemonstrate that the proposed TRIVR achieves a significant and consistentperformance gain compared with the state-of-the-art models.

Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction