  One of appealing approaches to guiding learnable parameter optimization, such
as feature maps, is global attention, which enlightens network intelligence at
a fraction of the cost. However, its loss calculation process still falls
short: 1)We can only produce one-dimensional 'pseudo labels' for attention,
since the artificial threshold involved in the procedure is not robust; 2) The
attention awaiting loss calculation is necessarily high-dimensional, and
decreasing it by convolution will inevitably introduce additional learnable
parameters, thus confusing the source of the loss. To this end, we devise a
simple but efficient Indirect-Instant Attention Optimization (IIAO) module
based on SoftMax-Attention , which transforms high-dimensional attention map
into a one-dimensional feature map in the mathematical sense for loss
calculation midway through the network, while automatically providing adaptive
multi-scale fusion to feature pyramid module. The special transformation yields
relatively coarse features and, originally, the predictive fallibility of
regions varies by crowd density distribution, so we tailor the Regional
Correlation Loss (RCLoss) to retrieve continuous error-prone regions and smooth
spatial information . Extensive experiments have proven that our approach
surpasses previous SOTA methods in many benchmark datasets.
