Review for NeurIPS paper: Distribution Matching for Crowd Counting

Weaknesses: 1. Novelty - The contributions of the paper in terms of novelty are: (i) The idea of using OT based distribution matching loss, (ii) theoretical results showing that such loss results in lower error, (iii) empirical results showing that such a loss indeed results in lower error. For the crowd counting community, this may be considered as considerable contributions. However, the paper does not address if this is of interest to the broader vision/ml community - which is expected for a Neurips kind of venue. For example, the authors could have considered a broader set of applications like object detection for evaluating their method. Further, the authors should have given a better background for the recent works that have focussed on improving representation of ground-truth [2,9,10] for training the networks.