Abstract
Conventional object detection techniques can require extensive, task (specific) retraining to adapt to new domains or evolving conditions, which may be inefficient for certain applications. A system and method are described that can utilize a generative model-based workflow to perform context-aware object detection. For a given input, such as an image, and a textual prompt, a generative model may be invoked multiple times concurrently to produce a diverse set of candidate detections. A consensus mechanism can then process this set of candidates, for example, by filtering for duplicates and synthesizing the varied information to create a consolidated set of results. This approach may allow the system to adapt to new tasks and dynamic environments based on descriptive instructions, potentially reducing the need for model retraining and specialized datasets.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kuligin, Leonid and Zettl, Thomas, "Generative Model-Based Object Detection Using Concurrent Candidate Generation and Consensus", Technical Disclosure Commons, (May 25, 2026)
https://www.tdcommons.org/dpubs_series/10228