TITLE: InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion 

ABSTRACT: This paper introduces a novel approach to evaluating deep learning models'
capacity for in-diagram logic interpretation. Leveraging the intriguing realm
of visual illusions, we establish a unique dataset, InDL, designed to
rigorously test and benchmark these models. Deep learning has witnessed
remarkable progress in domains such as computer vision and natural language
processing. However, models often stumble in tasks requiring logical reasoning
due to their inherent 'black box' characteristics, which obscure the
decision-making process. Our work presents a new lens to understand these
models better by focusing on their handling of visual illusions -- a complex
interplay of perception and logic. We utilize six classic geometric optical
illusions to create a comparative framework between human and machine visual
perception. This methodology offers a quantifiable measure to rank models,
elucidating potential weaknesses and providing actionable insights for model
improvements. Our experimental results affirm the efficacy of our benchmarking
strategy, demonstrating its ability to effectively rank models based on their
logic interpretation ability. As part of our commitment to reproducible
research, the source code and datasets will be made publicly available at
https://github.com/rabbit-magic-wh/InDL
This paper introduces a novel approach to evaluating deep learning models'
capacity for in-diagram logic interpretation. Leveraging the intriguing realm
of visual illusions, we establish a unique dataset, InDL, designed to
rigorously test and benchmark these models. Deep learning has witnessed
remarkable progress in domains such as computer vision and natural language
processing. However, models often stumble in tasks requiring logical reasoning
due to their inherent 'black box' characteristics, which obscure the
decision-making process. Our work presents a new lens to understand these
models better by focusing on their handling of visual illusions -- a complex
interplay of perception and logic. We utilize six classic geometric optical
illusions to create a comparative framework between human and machine visual
perception. This methodology offers a quantifiable measure to rank models,
elucidating potential weaknesses and providing actionable insights for model
improvements. Our experimental results affirm the efficacy of our benchmarking
strategy, demonstrating its ability to effectively rank models based on their
logic interpretation ability. As part of our commitment to reproducible
research, the source code and datasets will be made publicly available at
https://github.com/rabbit-magic-wh/InDL
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion
http://arxiv.org/abs/2305.17716v4