An AI-powered framework for streamlined image and audio annotations

With AI applications scaling more rapidly and broadly than ever, there is a growing demand for large sets of high-quality labeled data. However, data annotation has traditionally been a time-consuming and laborintensive task, both for annotators, who elaborately submit annotations, and task supervisors, who manage and review the submitted work. This challenge has shifted research interest towards integrating AI methods into the annotation process, with the aim of reducing human effort. In this direction, we propose an end-to-end annotation pipeline, designed to support AI-assisted image and audio annotations. Role-based access to the app grants supervisors full control over the management of annotation tasks and distribution of annotation workload across multiple annotators. Its AI-centric architecture supports seamless integration of pre-trained AI models, which can assist annotators during the annotation process or even perform annotations fully autonomously, having a great impact in reducing effort. In the realm of annotation quality, a built-in mechanism provides analytics based on submitted annotations, by measuring annotator agreement and producing visual summaries of progress. Evaluated in complex annotation tasks, the proposed framework is proven to be highly effective in reducing annotation time and effort. As a future direction, the proposed framework provides a strong foundation for integrating active learning techniques, further minimizing annotation costs.