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

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

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.