Weakly supervised one-shot image segmentation

A computer-implemented method, comprising:

• training, in a supervised manner, a machine learning model to learn a plurality of base class prototypes corresponding to a plurality of base objects, each of the plurality of base class prototypes corresponding to a segmentation of a class of one or more similar base objects;
• training, based at least on a support image depicting a novel object, the machine learning model to learn a novel class prototype corresponding to the novel object, the machine learning model being trained to learn the novel class prototype based at least on one of the plurality of base class prototypes identified as being similar to the support image, and the novel object being a different object than the plurality of base objects;
• and
• applying the trained machine learning model to segment a query image.

A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising:

• training, in a supervised manner, a machine learning model to learn a plurality of base class prototypes corresponding to a plurality of base objects, each of the plurality of base class prototypes corresponding to a segmentation of a class of one or more similar base objects;
• training, based at least on a support image depicting a novel object, the machine learning model to learn a novel class prototype corresponding to the novel object, the machine learning model being trained to learn the novel class prototype based at least on one of the plurality of base class prototypes identified as being similar to the support image, and the novel object being a different object than the plurality of base objects;
• and
• applying the trained machine learning model to segment a query image.