Can We Automate Diagrammatic Reasoning?

  title={Can We Automate Diagrammatic Reasoning?},
  author={Sk. Arif Ahmed and Debi Prosad Dogra and Samarjit Kar and Partha Pratim Roy and D. Prasad},
Learning to solve diagrammatic reasoning (DR) can be a challenging but interesting problem to the computer vision research community. It is believed that next generation pattern recognition applications should be able to simulate human brain to understand and analyze reasoning of images. However, due to the lack of benchmarks of diagrammatic reasoning, the present research primarily focuses on visual reasoning that can be applied to real-world objects. In this paper, we present a diagrammatic… 
1 Citations
Two-stage Rule-induction Visual Reasoning on RPMs with an Application to Video Prediction
  • Wentao He, Jianfeng Ren, Ruibin Bai, Xudong Jiang
  • Computer Science
  • 2021
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.


RAVEN: A Dataset for Relational and Analogical Visual REasoNing
This work proposes a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation and establishes a semantic link between vision and reasoning by providing structure representation.
Learning to Make Analogies by Contrasting Abstract Relational Structure
This work studies how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data and finds that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model.
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
This work presents a diagnostic dataset that tests a range of visual reasoning abilities and uses this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.
Inferring and Executing Programs for Visual Reasoning
A model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer is proposed.
Modeling Visual Problem Solving as Analogical Reasoning
The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly, and shows that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solve, and reasoning more generally, at the highest level.
Measuring abstract reasoning in neural networks
A dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test, is proposed and ways to both measure and induce stronger abstract reasoning in neural networks are introduced.
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
A framework, Neural Logic Programming, is proposed that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model and outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.
Learning to Reason: End-to-End Module Networks for Visual Question Answering
End-to-End Module Networks are proposed, which learn to reason by directly predicting instance-specific network layouts without the aid of a parser, and achieve an error reduction of nearly 50% relative to state-of-theart attentional approaches.
Artificial intelligence: Deep neural reasoning
  • H. Jaeger
  • Computer Science, Medicine
  • 2016
A hybrid learning machine that is composed of a neural network that can read from and write to an external memory structure analogous to the random-access memory in a conventional computer, which can learn to plan routes on the London Underground and achieve goals in a block puzzle, merely by trial and error.
Show and tell: A neural image caption generator
This paper presents a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.