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Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. Its goal is to drastically prune the number of overlapping detected candidate regions-of-interest (ROIs) and replace them with a single, more spatially accurate detection. The default algorithm (Greedy NMS) is fairly simple and suffers from drawbacks, due to its need for manual tuning. Recently, NMS has been improved using deep neural networks that learn how to solve a spatial overlap-based detections rescoring task in a supervised manner, where only ROI coordinates are exploited as input. In this paper, neural NMS performance is augmented by feeding the network additional information extracted from the appearance of each candidate ROI. This information captures statistical properties regarding the spatial distribution of interest-points detected within the corresponding image region. Thus, the deviation in 2D …