1. A major concern that I have is, the authors consider only shifts in annotations as the noise. However, real-world annotations include other types of noises like missing annotations or duplicate annotations. The authors do not consider this in their discussion. From the outset, it seems that their current method cannot accommodate these additional noises. From this perspective, I would say that the paper is incomplete in modeling different types of annotation noises. 2. It is not clear why the authors approximate pdf phi and Psi with Gaussian distribution. 3. It is not clear why eta_ri term is non-central chi-squared distribution. 4. As far as I understand, small shifts in annotations will not affect performance much, since neural networks can be robust if receptive size of the network is large enough. Can the authors discuss this more in detail. 5. The proposed method seems to be too specific to the counting problem. Can this method be extended to other problems in vision like object detection.
3. It is not clear why eta_ri term is non-central chi-squared distribution.