Systems and methods for training multi-class object classification models with partially labeled training data

A computing system for training a multi-class object classification model with partially labeled training data, comprising:

• one or more processors;
• a machine-learned multi-class object classification model configured to classify a plurality of object classes;
• and
• one or more tangible, non-transitory computer readable media storing computer-readable instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising:
• obtaining image data depicting one or more objects and ground truth data comprising a subset of object class annotations respectively associated with a subset of object classes of the plurality of object classes;
• processing the image data with the machine-learned multi-class object classification model to obtain object classification data;
• evaluating a loss function that evaluates a multi-class classification loss comprising a difference between the object classification data and the subset of object class annotations, wherein the loss function comprises a plurality of weighted loss signals respectively associated with the plurality of object classes, wherein the weight of each of the weighted loss signals has a magnitude greater than zero and is based at least in part on the inclusion of the object class associated with the respective loss signal within the subset of object classes;
• and
• adjusting one or more parameters of the machine-learned multi-class object classification model based at least in part on the loss function.