Adding a second layer of meta-data to your model
Attributors are used in combination with other models such as object detectors, semantic and instance segmentors.
They add a layer of meta-data to your model. First, a detection or segmentation model creates predictions, and then the attributor runs another classification on top of this prediction. Chaining the models this way allows you to break down a task into smaller sub-tasks, which often yields better results.
A good example use case for attributors is defect detection in manufacturing. Often, first, an instance segmentor is trained to find a defect, and then an attributor assesses its severity.
When annotating data, attributes can also be used to create second-degree taxonomies:

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