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

该项目为计算机视觉数据提供了可重复性，低开销，研究的可重复性和持久性。 因此，该项目正在设定一个方向，计算机视觉社区可以共同努力创建一个数据集基础设施，该基础设施允许跨单个数据集和注释的透明度，具有社区设置的语料库和指标的实验基准，以及基于Web的基础设施，以培养计算机视觉数据集的持续发展。 这种基础设施的可用性被命名为COVE：计算机视觉数据、注释和工具交换，它影响了计算机视觉和相关社区，以开发下一代强大的智能能力，这些能力具有巨大的积极影响社会的潜力。该项目通过支持研究生和本科生与教育相结合，并通过外联活动接触中学生，该项目正在建立COVE，这是一个由社区管理的中央基础设施，以支持数据和注释的交换以及操作它们的软件工具。 该基础设施是基于网络的开放源代码，并提供对其内容的开放访问。内容的管理最初由研究者管理，随后通过计算机视觉社区的当选成员进行管理。基础设施有两个突出的组成部分。首先，策展基础设施促进了后端存储，查询，数据注释和策展工具，以支持它。为了策展联合数据集，COVE使用了众所周知的开源工具，如Python，Bootstrap和Postgresql。 为了将新注释纳入交易所，该项目在很大程度上依赖于众包。 第二，使用基础设施，例如，数据结构和软件使得研究人员和从业人员能够广泛和容易地使用。 该项目开发了API，允许通过Matlab和OpenCV等通用软件接口轻松编程访问联邦数据集和工具。

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.