Learning semantic segmentation models in the absence of a portion of class labels

A system for performing semantic segmentation in an absence of labels for one or more semantic classes, comprising:

• one or more processors programmed to:
• utilize one or more weak predictors to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes;
• merge the label proposals with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset;
• train a machine learning model using the merged dataset;
• and
• utilize the machine learning model for performing semantic segmentation on image data.

A non-transitory computer-readable medium comprising instructions for performing semantic segmentation in an absence of labels for one or more semantic classes, that, when executed by one or more processors case the one or more processors to perform operations including to:

• utilize one or more weak predictors to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes;
• merge the label proposals with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset;
• train a machine learning model using the merged dataset;
• and
• utilize the machine learning model for performing semantic segmentation on image data.