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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 intrinsic natures of RPM problem, 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 state-of-the-art models. • A two-stage rule-induction visual reasoner is proposed to solve RPMs. • The proposed method generates one reasoning rule for each sample question. • The proposed method yields reasoning rules modeling human in solving RPMs. • A real-world RPM-like video prediction dataset is developed for evaluation.",[11,12,13,14],"He, Wentao","Ren, Jianfeng","Bai, Ruibin","Jiang, Xudong",[16,17,18,19],"0000-0002-6319-1639","0000-0003-4619-6590","0000-0003-1722-568X","0000-0002-9104-2315",2024,"https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.patcog.2024.111151","W4404171653","Pattern Recognition",null,[26,27,28,29,30,31],"Stage (stratigraphy)","Computer science","Rule induction","Artificial intelligence","Machine learning","Pattern recognition 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