标题： BOLD5000：5000张图像的公开fMRI数据集 显示英文标题

标题： BOLD5000: A public fMRI dataset of 5000 images

摘要： 视觉科学，特别是机器视觉，由于引入了大规模图像数据集和统计学习方法而发生了革命性的变化。 然而，人类神经成像研究的视觉感知仍然依赖于少量的图像（大约100张），这是由于时间受限的实验程序。 为了应用整合神经科学的统计学习方法，神经成像中使用的图像数量必须显著增加。 我们提出了BOLD5000，一项包含几乎5,000张不同图像的人类功能磁共振成像（fMRI）研究，这些图像描绘了现实世界场景。 除了相对于以前的fMRI研究大幅增加了图像数据集的规模，BOLD5000还考虑了图像的多样性，通过纳入来自场景理解（SUN）、上下文中的常见物体（COCO）和ImageNet数据集的图像，与标准计算机视觉数据集有所重叠。 这些图像数据集的规模和多样性，结合缓慢的事件相关fMRI设计，使得对广泛范围的视觉特征、类别和语义的神经表征进行细致探索成为可能。 同时，BOLD5000使我们更接近实现马尔关于单一视觉科学的梦想——生物视觉和计算机视觉的交织研究。 显示英文摘要

摘要： Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies of visual perception still rely on small numbers of images (around 100) due to time-constrained experimental procedures. To apply statistical learning approaches that integrate neuroscience, the number of images used in neuroimaging must be significantly increased. We present BOLD5000, a human functional MRI (fMRI) study that includes almost 5,000 distinct images depicting real-world scenes. Beyond dramatically increasing image dataset size relative to prior fMRI studies, BOLD5000 also accounts for image diversity, overlapping with standard computer vision datasets by incorporating images from the Scene UNderstanding (SUN), Common Objects in Context (COCO), and ImageNet datasets. The scale and diversity of these image datasets, combined with a slow event-related fMRI design, enable fine-grained exploration into the neural representation of a wide range of visual features, categories, and semantics. Concurrently, BOLD5000 brings us closer to realizing Marr's dream of a singular vision science - the intertwined study of biological and computer vision.