  Because of the powerful learning capability of deep neural networks, counting
performance via density map estimation has improved significantly during the
past several years. However, it is still very challenging due to severe
occlusion, large scale variations, and perspective distortion. Scale variations
(from image to image) coupled with perspective distortion (within one image)
result in huge scale changes of the object size. Earlier methods based on
convolutional neural networks (CNN) typically did not handle this scale
variation explicitly, until Hydra-CNN and MCNN. MCNN uses three columns, each
with different filter sizes, to extract features at different scales. In this
paper, in contrast to using filters of different sizes, we utilize an image
pyramid to deal with scale variations. It is more effective and efficient to
resize the input fed into the network, as compared to using larger filter
sizes. Secondly, we adaptively fuse the predictions from different scales
(using adaptively changing per-pixel weights), which makes our method adapt to
scale changes within an image. The adaptive fusing is achieved by generating an
across-scale attention map, which softly selects a suitable scale for each
pixel, followed by a 1x1 convolution. Extensive experiments on three popular
datasets show very compelling results.
