【摘要】To address the scale variance and uneven distribution of objects in scenarios of object-counting tasks,an algorithm called Refinement Network(RefNet) is exploited.The proposed top-down scheme sequentially aggregates multiscale features,which are laterally connected with low-level information.Trained by a multiresolution density regression loss,a set of intermediate-density maps are estimated on each scale in a multiscale feature pyramid,and the detailed information of the density map is gradually added through coarse-to-fine granular refinement progress to predict the final density map.We evaluate our RefNet on three crowd-counting benchmark datasets,namely,ShanghaiTech,UCF_CC_50,and UCSD,and our method achieves competitive performances on the mean absolute error and root mean squared error compared to the state-of-the-art approaches.We further extend our RefNet to cell counting,illustrating its effectiveness on relative counting tasks.