Counting with limited supervision - Hobley, MA (2023)
Counting is among the first abstract analysis tasks that we learn. It is the most fundamental way we can quantitatively understand data. From an early age, people are able to accurately count objects with minimal direction, even when the type of object to be counted is completely unknown. While prior machine learning-based methods have addressed the problem of counting previously unseen kinds of objects, known as class-agnostic counting, they have all required both user input during deployment in the form of exemplar images to define the type to be counted and the locations of every object during training to act as supervision.