Taylor - Impersonation of AI for Audiovisual Content Documentation and Search

While AI-based audiovisual analysis tools have without doubt made huge progress, integrating them in media production and archiving workflows is still challenging, as the provided annotations may not match needs in terms of type, granularity and accuracy of metadata, and do not well align with existing workflows. We propose a system for annotation and search in media archive applications, using a range of AI-based analysis methods. In order to facilitate communication of explanations and collect relevance feedback, an impersonation of the systems' intelligence, named Taylor, is included as an element of the user interface.

Automated summary

While AI-based audiovisual analysis tools have made significant progress, integrating them into media production and archiving workflows remains challenging due to mismatched annotations, lack of alignment with existing workflows, and the need for accurate metadata. We propose a system that utilizes AI-based analysis methods for annotation and search in media archive applications. To enhance communication and gather feedback, Taylor, an artificial intelligence agent, is included in the user interface.