English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of appealing approaches to guiding learnable parameter optimization, suchas feature maps, is global attention, which enlightens network intelligence ata fraction of the cost. However, its loss calculation process still fallsshort: 1)We can only produce one-dimensional 'pseudo labels' for attention,since the artificial threshold involved in the procedure is not robust; 2) Theattention awaiting loss calculation is necessarily high-dimensional, anddecreasing it by convolution will inevitably introduce additional learnableparameters, thus confusing the source of the loss. To this end, we devise asimple but efficient Indirect-Instant Attention Optimization (IIAO) modulebased on SoftMax-Attention , which transforms high-dimensional attention mapinto a one-dimensional feature map in the mathematical sense for losscalculation midway through the network, while automatically providing adaptivemulti-scale fusion to feature pyramid module. The special transformation yieldsrelatively coarse features and, originally, the predictive fallibility ofregions varies by crowd density distribution, so we tailor the RegionalCorrelation Loss (RCLoss) to retrieve continuous error-prone regions and smoothspatial information . Extensive experiments have proven that our approachsurpasses previous SOTA methods in many benchmark datasets.

Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes