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We implement a real-time object naming system that enables learning a set ofnamed entities never seen. Our approach employs an existing foundation modelthat we consider ready to see anything before starting. It turns seen imagesinto relatively small feature vectors that we associate with index to agradually built vocabulary without any training of fine-tuning of the model.Our contribution is using the association mechanism known from transformers asattention. It has features that support generalization from irrelevantinformation for distinguishing the entities and potentially enable associatingwith much more than indices to vocabulary. As a result, the system can work ina one-shot manner and correctly name objects named in different contents. Wealso outline implementation details of the system modules integrated by ablackboard architecture. Finally, we investigate the system's quality, mainlyhow many objects it can handle in this way.

Tuning-less Object Naming with a Foundation Model