Deep visual recognition models have seen substantial performance improvements in recent years but are still typically trained for only one specific task, such as segmentation, classification, etc. Although these models often have the same core architectural backbone, there is no existing method for easily combining multiple task-specific models into one that can handle multiple tasks.