ConCoNet: Class-agnostic counting with positive and negative exemplars

Class-agnostic counting is usually phrased as a matching problem between a user-defined exemplar patch and a query image. The count is derived based on the number of objects similar to the exemplar patch. However, defining a target class using only positive exemplar patches inevitably miscounts unintended objects that are visually alike to the exemplar. In this paper, we propose to include negative exemplars that define what not to count. This allows the model to calibrate its notion of what is similar based on both positive and negative exemplars. It effectively disentangles visually similar negatives, leading to a more discriminative definition of the target object. We designed our method such that it can be incorporated with other class-agnostic counting models. Moreover, application-wise, our model can be used into a semi-automatic labeling tool to simplify the job of the annotator