BRI3L：用于视错觉感知区域识别与定位的亮度错觉图像数据集
BRI3L: A Brightness Illusion Image Dataset for Identification and Localization of Regions of Illusory Perception
AI总结：
该论文构建了包含22,366张图像的五类亮度错觉数据集BRI3L，并采用神经网络实现错觉分类与定位，分别达到99.56%和84.37%的准确率，且模型可泛化至未见错觉类型。
摘要：
视觉错觉在理解视觉感知中扮演着重要角色。当前理解和评估视觉错觉的方法大多基于确定性滤波，并且仅在少数视觉错觉上进行评估，因此结论不具有普遍性。为此，我们生成了一个包含22,366张图像的大规模数据集（BRI3L：用于错觉感知识别与定位的亮度错觉图像数据集），涵盖五种亮度错觉类型，并使用基于数据驱动的神经网络方法对该数据集进行基准测试。该数据集包含标签信息：（1）特定图像是否为错觉/非错觉；（2）图像错觉区域的分割掩码。因此，分类和分割任务均可使用该数据集进行评估。我们遵循涉及人类受试者的标准心理物理学实验来验证数据集。据我们所知，这是首次尝试开发视觉错觉数据集并使用数据驱动方法进行错觉分类和定位的基准测试。我们考虑了五种研究充分的亮度错觉类型：1）赫尔曼网格，2）同时亮度对比，3）怀特错觉，4）网格错觉，5）感应光栅错觉。在该数据集上的基准测试在错觉识别中达到了99.56%的准确率，在错觉定位中达到了84.37%的像素准确率。研究表明，深度学习模型的应用也能泛化到未见过的亮度错觉，如从亮度同化到对比过渡。我们还测试了最先进的扩散模型生成亮度错觉的能力。我们已在GitHub仓库中提供了所有代码、数据集、说明等：https://github.com/aniket004/BRI3L
英文摘要：
Visual illusions play a significant role in understanding visual perception. Current methods in understanding and evaluating visual illusions are mostly deterministic filtering based approach and they evaluate on a handful of visual illusions, and the conclusions therefore, are not generic. To this end, we generate a large-scale dataset of 22,366 images (BRI3L: BRightness Illusion Image dataset for Identification and Localization of illusory perception) of the five types of brightness illusions and benchmark the dataset using data-driven neural network based approaches. The dataset contains label information - (1) whether a particular image is illusory/nonillusory, (2) the segmentation mask of the illusory region of the image. Hence, both the classification and segmentation task can be evaluated using this dataset. We follow the standard psychophysical experiments involving human subjects to validate the dataset. To the best of our knowledge, this is the first attempt to develop a dataset of visual illusions and benchmark using data-driven approach for illusion classification and localization. We consider five well-studied types of brightness illusions: 1) Hermann grid, 2) Simultaneous Brightness Contrast, 3) White illusion, 4) Grid illusion, and 5) Induced Grating illusion. Benchmarking on the dataset achieves 99.56% accuracy in illusion identification and 84.37% pixel accuracy in illusion localization. The application of deep learning model, it is shown, also generalizes over unseen brightness illusions like brightness assimilation to contrast transitions. We also test the ability of state-of-theart diffusion models to generate brightness illusions. We have provided all the code, dataset, instructions etc in the github repo: https://github.com/aniket004/BRI3L
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
