A sparse contrastive loss is further proposed to train our model with sparse supervision. Our new formulation generates a 3D heatmap indicating the plausibility of all spot/scale combinations in a single network forward go, which is around 10 instances quicker in comparison to the preceding sliding-window approach. Additionally, it supports interac