People, Penguins and Petri Dishes: Adapting Object Counting Models To New
Visual Domains And Object Types Without Forgetting
Abstract
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving
the original counting function. Domain-specific normalisation and scaling operators are trained to allow the model
to adjust to the statistical distributions of the various visual
domains. The developed adaptation technique is used to
produce a singular patch-based counting regressor capable
of counting various object types including people, vehicles,
cell nuclei and wildlife. As part of this study a challenging
new cell counting dataset in the context of tissue culture
and patient diagnosis is constructed. This new collection,
referred to as the Dublin Cell Counting (DCC) dataset, is the
first of its kind to be made available to the wider computer
vision community. State-of-the-art object counting performance is achieved in both the Shanghaitech (parts A and
B) and Penguins datasets while competitive performance
is observed on the TRANCOS and Modified Bone Marrow
(MBM) datasets, all using a shared counting model.