Generating Correct Answers for Progressive Matrices Intelligence Tests
Raven's Progressive Matrices are multiple-choice intelligence tests, where one tries to complete the missing location in a $3\times 3$ grid of abstract images. Previous attempts to address this test have focused solely on selecting the right answer out of the multiple choices. In this work, we focus, instead, on generating a correct answer given the grid, without seeing the choices, which is a harder task, by definition. The proposed neural model combines multiple advances in generative models, including employing multiple pathways through the same network, using the reparameterization trick along two pathways to make their encoding compatible, a dynamic application of variational losses, and a complex perceptual loss that is coupled with a selective backpropagation procedure. Our algorithm is able not only to generate a set of plausible answers, but also to be competitive to the state of the art methods in multiple-choice tests.
Code (0)
등록된 구현이 없습니다.
Tasks
Multiple-choiceSimilar Papers 제목 키워드 기반
Data augmentation by morphological mixup for solving Raven's Progressive Matrices
Raven's Progressive Matrices (RPMs) are frequently used in testing human's visual reasoning ability. Recent advances of RPM-like datasets and solution models partially address the challenges of visually understanding the…
Data AugmentationVisual ReasoningRaven's Progressive Matrices Completion with Latent Gaussian Process Priors
Abstract reasoning ability is fundamental to human intelligence. It enables humans to uncover relations among abstract concepts and further deduce implicit rules from the relations. As a well-known abstract visual reason…
Answer SelectionGaussian ProcessesVisual ReasoningUnsupervised Abstract Reasoning for Raven's Problem Matrices
Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans. In this paper, to study the abstract reasoning capability …
The Potential of LLMs in Medical Education: Generating Questions and Answers for Qualification Exams
Recent research on large language models (LLMs) has primarily focused on their adaptation and application in specialized domains. The application of LLMs in the medical field is mainly concentrated on tasks such as the a…
DiagnosticMedical Report GenerationVisual-Imagery-Based Analogical Construction in Geometric Matrix Reasoning Task
Raven's Progressive Matrices is a family of classical intelligence tests that have been widely used in both research and clinical settings. There have been many exciting efforts in AI communities to computationally model…