  Generic object counting in natural scenes is a challenging computer vision
problem. Existing approaches either rely on instance-level supervision or
absolute count information to train a generic object counter. We introduce a
partially supervised setting that significantly reduces the supervision level
required for generic object counting. We propose two novel frameworks, named
lower-count (LC) and reduced lower-count (RLC), to enable object counting under
this setting. Our frameworks are built on a novel dual-branch architecture that
has an image classification and a density branch. Our LC framework reduces the
annotation cost due to multiple instances in an image by using only lower-count
supervision for all object categories. Our RLC framework further reduces the
annotation cost arising from large numbers of object categories in a dataset by
only using lower-count supervision for a subset of categories and class-labels
for the remaining ones. The RLC framework extends our dual-branch LC framework
with a novel weight modulation layer and a category-independent density map
prediction. Experiments are performed on COCO, Visual Genome and PASCAL 2007
datasets. Our frameworks perform on par with state-of-the-art approaches using
higher levels of supervision. Additionally, we demonstrate the applicability of
our LC supervised density map for image-level supervised instance segmentation.
