利用计算机视觉估计自然场景中数量与非数值视觉量级的分布
Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision
AI总结：
本文提出基于计算机视觉的图像分析流程，在数千张真实日常图像中估计数量与非数值视觉量级分布，发现数量频率呈幂律且相关结构跨数据集稳定。
摘要：
人类与许多动物物种都具有感知并近似表征视觉场景中物体数量的能力。这种能力在整个童年时期不断提升，表明学习和发育在塑造我们的数感方面发挥着关键作用。基于深度学习的计算研究进一步支持了这一假设，这些研究表明，在学习包含不同数量项目的图像的统计结构时，数量感知可以在神经网络中自发涌现。然而，神经网络模型通常使用合成数据集进行训练，这些数据集可能无法真实反映自然环境的统计结构；同时，人们也越来越关注使用更具生态性的视觉刺激来研究人类的数量感知。在本研究中，我们利用计算机视觉算法的最新进展，设计并实现了一个原创流程，可用于估计大规模数据集中数量和非数值量级的分布；这些数据集包含数千张描绘日常生活情境中物体的真实图像。我们表明，在自然视觉场景中，不同数量的出现频率遵循幂律分布。此外，我们还表明，数量与连续量级之间的相关结构在不同数据集和场景类型（同质物体集合与异质物体集合）之间保持稳定。我们认为，考虑这种“生态性”协方差模式，对于理解非数值视觉线索对数量判断的影响非常重要。
英文摘要：
Humans share with many animal species the ability to perceive and approximately represent the number of objects in visual scenes. This ability improves throughout childhood, suggesting that learning and development play a key role in shaping our number sense. This hypothesis is further supported by computational investigations based on deep learning, which have shown that numerosity perception can spontaneously emerge in neural networks that learn the statistical structure of images with a varying number of items. However, neural network models are usually trained using synthetic datasets that might not faithfully reflect the statistical structure of natural environments, and there is also growing interest in using more ecological visual stimuli to investigate numerosity perception in humans. In this work, we exploit recent advances in computer vision algorithms to design and implement an original pipeline that can be used to estimate the distribution of numerosity and non-numerical magnitudes in large-scale datasets containing thousands of real images depicting objects in daily life situations. We show that in natural visual scenes the frequency of appearance of different numerosities follows a power law distribution. Moreover, we show that the correlational structure for numerosity and continuous magnitudes is stable across datasets and scene types (homogeneous vs. heterogeneous object sets). We suggest that considering such "ecological" pattern of covariance is important to understand the influence of non-numerical visual cues on numerosity judgements.
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。
