IntentVizor

Intent Vizor (GitHub Project) – Intent Vizor is the name of a GitHub-hosted research project that deals with video summarization guided by user queries. It appears to be an outcome of academic work (likely presented at a conference in 2021 or 2022) focused on interactive or query-driven video summaries.

The idea behind Intent Vizor is to allow a user to ask for specific information from a long video and get a tailored summary or highlight reel relevant to that query. For example, given a lengthy recording of a city council meeting, a user could query “road infrastructure” and the system would produce a short compilation of segments where road infrastructure is discussed.

The GitHub project likely contains code (maybe in Python with deep learning frameworks) that demonstrates how to perform this task: possibly using natural language processing to parse the query and video transcription, and computer vision to cut the appropriate scenes. It might use techniques like text embeddings (to understand the semantic intent of the query) and then rank or select video segments that match that intent. The interface could then let the user refine or interact (e.g., selecting which segments are most useful). Being a research project, Intent Vizor is probably not a commercial product but rather a prototype showing what’s possible – addressing the challenge that generic video summaries might not satisfy everyone, so a customizable summary by intent is more flexible. Its inclusion in the directory highlights cutting-edge developments in AI: moving beyond one-size-fits-all summarization to more interactive AI that can answer specific needs from multimedia content. In short, Intent Vizor is an experimental tool that lets you “query” a video as if it were a database, and get a concise visual answer, showcasing the future of how we might consume and navigate video information with the help of AI.