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We are interested in counting the number of instances of object classes innatural, everyday images. Previous counting approaches tackle the problem inrestricted domains such as counting pedestrians in surveillance videos. Countscan also be estimated from outputs of other vision tasks like object detection.In this work, we build dedicated models for counting designed to tackle thelarge variance in counts, appearances, and scales of objects found in naturalscenes. Our approach is inspired by the phenomenon of subitizing - the abilityof humans to make quick assessments of counts given a perceptual signal, forsmall count values. Given a natural scene, we employ a divide and conquerstrategy while incorporating context across the scene to adapt the subitizingidea to counting. Our approach offers consistent improvements over numerousbaseline approaches for counting on the PASCAL VOC 2007 and COCO datasets.Subsequently, we study how counting can be used to improve object detection. Wethen show a proof of concept application of our counting methods to the task ofVisual Question Answering, by studying the `how many?' questions in the VQA andCOCO-QA datasets.

Counting Everyday Objects in Everyday Scenes