CI-NEW: Collaborative Research: COVE-Computer Vision Exchange for Data, Annotations and Tools

CI-NEW：协作研究：COVE-数据、注释和工具的计算机视觉交换

• 批准号: 1629700
• 负责人: Kate Saenko
• 金额: $ 20.6万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629700&HistoricalAwards=false
• 关键词: CI; NEW; Collaborative; Research; COVE

The project provides discoverability, low overhead for use, reproducibility of research, and persistence for computer vision data. The project is hence setting a direction toward which the computer vision community can collectively work in creating a dataset infrastructure that allows for transparency across individual datasets and annotations, experimental benchmarks with community-set corpora and metrics, and a web-based infrastructure to cultivate continued development of computer vision datasets. The availability of such an infrastructure, which is named COVE: Computer Vision Exchange of Data, Annotations and Tools, impacts the computer vision and related communities to develop next generation robust intelligence capabilities that have great potential to positively impact society. The project is integrated with education by supporting graduate and undergraduate students, and reaches middle school students through outreach activities.The project is establishing COVE, a centralized community-run infrastructure to support the exchange of data and annotations as well as the software tools to manipulate them. The infrastructure is web-based open-source, and provides open access to its contents. Stewardship over the contents are managed by the Investigators initially and subsequently through elected members of the computer vision community. There are two salient components of the infrastructure. First, a curation infrastructure facilitates back-end storage, querying, data annotation and curation tools, to support it. To curate the federated data set, COVE uses widely known open-source tools like Python, Bootstrap and Postgresql. For curation of new annotations to incorporate into the exchange, the project relies heavily on crowd-sourcing. Second, a usage infrastructure, e.g., data structures and software enables widespread and easy use by researchers and practitioners. The project develops APIs to allow for easy programmable access to the federated data sets and tools through common software interfaces like Matlab and OpenCV.

该项目提供了可发现性、低使用开销、研究的可重复性以及计算机视觉数据的持久性。 因此，该项目设定了一个方向，计算机视觉社区可以共同努力创建一个数据集基础设施，该基础设施允许各个数据集和注释之间的透明度、社区设置的语料库和指标的实验基准，以及基于网络的基础设施，以培养持续的计算机视觉数据集的开发。 这种基础设施的可用性被称为 COVE：计算机视觉数据、注释和工具交换，它会影响计算机视觉和相关社区开发下一代强大的智能能力，这些能力具有对社会产生积极影响的巨大潜力。该项目通过支持研究生和本科生与教育相结合，并通过外展活动覆盖中学生。该项目正在建立 COVE，这是一个集中的社区运营的基础设施，用于支持数据和注释的交换以及用于操作的软件工具他们。 该基础设施是基于网络的开源，并提供对其内容的开放访问。内容的管理权最初由研究人员管理，随后由计算机视觉社区的当选成员管理。基础设施有两个重要组成部分。首先，管理基础设施有助于后端存储、查询、数据注释和管理工具来支持它。 为了管理联合数据集，COVE 使用 Python、Bootstrap 和 Postgresql 等众所周知的开源工具。 为了将新注释纳入交换中，该项目在很大程度上依赖于众包。 其次，使用基础设施（例如数据结构和软件）使研究人员和从业者能够广泛且轻松地使用。 该项目开发了 API，以便通过 Matlab 和 OpenCV 等通用软件接口轻松编程访问联合数据集和工具。