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State-of-the-art methods use regression-based optimization\nwhere they explicitly learn to count the objects of interest. These often\nperform better than detection-based methods that need to learn the more\ndifficult task of predicting the location, size, and shape of each object.\nHowever, we propose a detection-based method that does not need to estimate the\nsize and shape of the objects and that outperforms regression-based methods.\nOur contributions are three-fold: (1) we propose a novel loss function that\nencourages the network to output a single blob per object instance using\npoint-level annotations only; (2) we design two methods for splitting large\npredicted blobs between object instances; and (3) we show that our method\nachieves new state-of-the-art results on several challenging datasets including\nthe Pascal VOC and the Penguins dataset. 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