Comp Cog: Collaborative Research on the Development of Visual Object Recognition

人类视觉目标识别是一种快速、稳健的方法。人们可以在复杂的场景中、从不同的视角以及在不太理想的环境中识别大量的视觉对象。这种能力是许多高级人类技能的基础，包括工具使用、阅读和导航。人工智能设备还没有达到日常人类物体识别的技能水平。这个项目将通过捕捉和分析1-2岁幼儿的视觉体验，解决当前知识中的一个空白，即对视觉体验的理解，从而发展出熟练的物体识别。这是理解人类视觉物体识别的关键时期，因为这是蹒跚学步的孩子学习大量物体类别的时候，当他们学习这些物体的名称时，当他们工具性地操作和使用物体作为工具的时候。与计算机视觉系统不同，两岁的孩子很快就能学会识别许多视觉物体。这个项目试图了解幼儿的训练经验(日常物体观看)如何对建立稳健的视觉物体识别是最佳的。该项目旨在(1)了解1至2岁儿童对常见物体(如杯子、椅子、卡车、狗)的视觉和统计规律，以及(2)确定类似于人类蹒跚学步儿童所经历的训练方案是否支持最先进的机器视觉识别视觉对象。通过研究“自然场景”的视觉统计数据，人们在理解成人视觉方面取得了长足的进步。然而，人们对这些场景中可能出现的伪影感到担忧，因为它们通常是由成年人拍摄的照片，因此可能会受到已经开发的成熟视觉系统的偏见，该系统可以固定相机并将照片框起来。此外，拍摄的场景与人们在世界上移动和行动时采样的场景有系统地不同。因此，人们对从身体佩戴的相机收集的以自我为中心的观点越来越感兴趣，这是本研究中使用的方法。学步儿童在进行日常活动时将佩戴轻便的头部摄像头，这样调查人员就可以捕捉到学步儿童看到的物体以及他们看到这些物体的视角和背景。这项研究将分析幼儿标准化学习的前100个物体名称的频率、视点、视觉特性和看到的物体的范围，为人类视觉物体识别的早期学习环境提供第一个描述。这些幼儿视角的场景将被用作机器学习模型的输入，以更好地理解场景中的视觉信息如何支持和约束视觉对象识别的发展。机器学习实验将确定哪些属性和统计规则对于学习识别多个场景环境中的常见对象类别最关键。收集的数据将通过数据库共享，这是一个开放的发展科学数据库。

Human visual object recognition is fast and robust. People can recognize a large number of visual objects in complex scenes, from varied views, and in less than optimal circumstances. This ability underlies many advanced human skills, including tool use, reading, and navigation. Artificial intelligence devices do not yet approach the level of skill of everyday human object recognition. This project will address one gap in current knowledge, an understanding of the visual experiences that allow skilled object recognition to develop, by capturing and analyzing the visual experiences of 1- to 2-year-old toddlers. This is a key period for understanding human visual object recognition because it is the time when toddlers learn a large number of object categories, when they learn the names for those objects, and when they instrumentally act on and use objects as tools. Two-year-old children, unlike computer vision systems, rapidly learn to recognize many visual objects. This project seeks to understand how the training experiences (everyday object viewing) of toddlers may be optimal for building robust visual object recognition. The project aims to (1) understand the visual and statistical regularities in 1- to 2-year-old children's experiences of common objects (e.g., cups, chairs, trucks, dogs) and (2) determine whether a training regimen like that experienced by human toddlers supports visual object recognition by state-of-the art machine vision. Considerable progress in understanding adult vision has been made by studying the visual statistics of "natural scenes." However, there is concern about possible artifacts in these scenes because they typically photographs taken by adults and thus potentially biased by the already developed mature visual system that holds the camera and frames the pictures. Also, photographed scenes differ systematically from the scenes sampled by people as they move about and act in the world. Accordingly, there is increased interest in egocentric views collected from body-worn cameras, the method used in the present work. Toddlers will wear lightweight head cameras as they go about their daily activities, allowing the investigators to capture the objects the toddlers see and the perspectives and contexts in which they see them. The research will analyze the frequency, views, visual properties, and range of seen objects for the first 100 object names normatively learned by young children, providing a first description of the early learning environment for human visual object recognition. These toddler-perspective scenes will be used as inputs to machine learning models to better understand how the visual information in the scenes supports and constrains the development of visual object recognition. Machine-learning experiments will determine which properties and statistical regularities are most critical for learning to recognize common object categories in multiple scene contexts. Data collected will be shared through Databrary, an open data library for developmental science.