Abstract
Common object counting in a natural scene is a challenging problem in
computer vision with numerous real-world applications. Existing image-level
supervised common object counting approaches only predict the global object
count and rely on additional instance-level supervision to also determine
object locations. We propose an image-level supervised approach that provides
both the global object count and the spatial distribution of object instances
by constructing an object category density map. Motivated by psychological
studies, we further reduce image-level supervision using a limited object count
information (up to four). To the best of our knowledge, we are the first to
propose image-level supervised density map estimation for common object
counting and demonstrate its effectiveness in image-level supervised instance
segmentation. Comprehensive experiments are performed on the PASCAL VOC and
COCO datasets. Our approach outperforms existing methods, including those using
instance-level supervision, on both datasets for common object counting.
Moreover, our approach improves state-of-the-art image-level supervised
instance segmentation with a relative gain of 17.8% in terms of average best
overlap, on the PASCAL VOC 2012 dataset.
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