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 here: \href{https://github.com/rabbit-magic-wh/InDL}{https://github.com/rabbit-magic-wh/InDL}.

翻译：本文提出了一种评估深度学习模型图内逻辑解读能力的新方法。借助视觉错觉这一引人入胜的领域，我们构建了名为InDL的独特数据集，旨在严格测试和基准化这些模型。深度学习在计算机视觉和自然语言处理等领域已取得显著进展。然而，由于模型固有的“黑箱”特性掩盖了决策过程，它们在需要逻辑推理的任务中常显不足。我们的工作通过聚焦模型对视觉错觉（一种感知与逻辑的复杂交织）的处理方式，为更好地理解这些模型提供了新视角。我们利用六种经典几何光学错觉，构建了人类与机器视觉感知的对比框架。该方法提供了可量化的模型排名度量，揭示了潜在弱点，并为模型改进提供了可行洞见。实验结果证实了我们基准测试策略的有效性，展示了其根据逻辑解读能力对模型进行有效排序的能力。为践行可重复研究的承诺，源代码与数据集将公开发布于：\href{https://github.com/rabbit-magic-wh/InDL}{https://github.com/rabbit-magic-wh/InDL}。