System for learning new visual inspection tasks using a few-shot meta-learning method

A non-transitory computer readable medium, storing instructions for executing a process, the instructions comprising:

• for an input of a first plurality of labeled images of a new domain task, processing the first plurality of labeled images through a plurality of backbone snapshots, each of the backbone snapshots representative of a model trained across a plurality of other domain tasks, each of the plurality of backbone snapshots configured to output a first plurality of features responsive to the input;
• processing a second input of second plurality of unlabeled images through the plurality of backbone snapshots to output a second plurality of features responsive to the second input;
• executing a feature transform process configured to transform the first plurality of features and the second plurality of features into similarity values that relate the first and the second plurality of features together based on similarity;
• executing a clustering process to cluster the first plurality of features and the second plurality of features based on the similarity values;
• and
• generating a representative model for the new domain task from the clustering and transformation of the first plurality of features and as associated from the clustered and transformed second plurality of features, wherein the new domain task is visual inspection, wherein the labeled images involve defect types for an object or task for the visual inspection, wherein the representative model is configured to output defects detected in the visual inspection.

An apparatus, comprising:

• a processor, configured to:
• for an input of a first plurality of labeled images of a new domain task, process the first plurality of labeled images through a plurality of backbone snapshots, each of the backbone snapshots representative of a model trained across a plurality of other domain tasks, each of the plurality of backbone snapshots configured to output a first plurality of features responsive to the input;
• process a second input of second plurality of unlabeled images through the plurality of backbone snapshots to output a second plurality of features responsive to the second input;
• execute a feature transform process configured to transform the first plurality of features and the second plurality of features into similarity values that relate the first and the second plurality of features together based on similarity;
• execute a clustering process to cluster the first plurality of features and the second plurality of features based on the similarity values;
• and
• generate a representative model for the new domain task from the clustering and transformation of the first plurality of features and as associated from the clustered and transformed second plurality of features, wherein the new domain task is visual inspection, wherein the labeled images involve defect types for an object or task for the visual inspection, wherein the representative model is configured to output defects detected in the visual inspection.