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

人类视觉对象识别是快速和鲁棒的。 人们可以在复杂的场景中识别大量的视觉对象，从不同的角度来看，在不太理想的情况下。 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。该项目将通过捕捉和分析1至2岁幼儿的视觉体验来解决当前知识中的一个差距，即对允许熟练物体识别发展的视觉体验的理解。 这是理解人类视觉物体识别的关键时期，因为这是幼儿学习大量物体类别的时间，当他们学习这些物体的名称时，以及当他们将物体作为工具使用时。 与计算机视觉系统不同，两岁的孩子很快就学会了识别许多视觉对象。 该项目旨在了解幼儿的训练经验（日常物体观察）如何最佳地建立强大的视觉物体识别。该项目旨在（1）了解1至2岁儿童对常见物体（例如，杯子、椅子、卡车、狗）和（2）确定像人类幼儿经历的训练方案是否支持通过现有技术的机器视觉进行的视觉对象识别。通过研究“自然场景”的视觉统计，在理解成人视觉方面取得了相当大的进展。“然而，人们担心这些场景中可能存在伪影，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影响，这些视觉系统可以容纳相机并为照片取景。此外，拍摄的场景系统地不同于人们在世界上移动和行动时所采样的场景。 因此，有越来越多的兴趣，在自我中心的意见，从身体佩戴的相机，在本工作中使用的方法收集。 幼儿在日常活动中将佩戴轻便的头部摄像机，使研究人员能够捕捉幼儿看到的物体以及他们看到这些物体的视角和背景。 该研究将分析频率，视图，视觉特性和范围看到的对象的前100个对象名称规范学习的幼儿，提供了人类视觉对象识别的早期学习环境的第一个描述。 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特性对于学习识别多个场景上下文中的常见对象类别最关键。 收集的数据将通过发育科学开放数据库Databaseary共享。

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.